Experimental Insight on the Impact of VoNR and VoLTE Calls on the Smartphone Battery Performance during Mobility in 5G SA Network

Experimental Insight on the Impact of VoNR and VoLTE Calls on the Smartphone Battery Performance during Mobility in 5G SA Network | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Experimental Insight on the Impact of VoNR and VoLTE Calls on the Smartphone Battery Performance during Mobility in 5G SA Network Manu Srivastava, Vimlesh Kumar Ray This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7754087/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The advent of 5G New Radio (NR) technology has introduced unprecedented capabilities in wireless communications, enabling enhanced throughput and sig- nificantly reduced latency. However, the substantial capabilities of 5G mobile communication have concurrently imposed considerable demands on the battery capacity of user equipment, particularly smartphones. 5G NR-capable smart- phones must support multiple NR frequency bands, utilize wide spectrum ranges through multiple antenna configurations, and maintain native voice call func- tionality in 5G Standalone (SA) networks via Voice over New Radio (VoNR). VoNR calls in 5G SA networks represent a critical native solution that presents significant opportunities for battery conservation. In this work, we conducted comprehensive experiments within live 5G SA network environments, utilizing smartphones equipped with all major chipset platforms available in the Android ecosystem. The primary objective was to investigate the impact on battery performance during active VoNR and VoLTE calls under mobility conditions, specifically when devices maintained Radio Resource Control (RRC) connected states. Our observations revealed that battery drain is higher during active VoNR calls in mobile scenarios com- pared to VoLTE calls. Notably, this finding contradicts results from previous laboratory-based studies, which suggested different performance characteristics. Furthermore, our experiments demonstrated that handover events occurred more frequently during VoLTE calls, a phenomenon that aligns with theoretical expec- tations in mobile network environments. The findings from this study provide valuable insights for Mobile Network Operators (MNOs) and Original Equip- ment Manufacturers (OEMs) regarding battery performance optimization during VoNR calls in mobile scenarios. This research contributes to the existing body of knowledge by offering practical insights that can inform future resource alloca- tion strategies while maintaining optimal user experience for 5G NR smartphone users. VoNR VoLTE Smartphone Mobility 5G Standalone Battery Consumption Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1 Introduction 5G New Radio communication is major milestone in mobile communication which has enables the efficient and dedicated voice packet transmission. Voice over New Radio (VoNR) in particular is crucial, which is native voice service in 5G Standalone (SA) Network. VoNR utilizes the full capacity of 5G NR SA network such as enhanced data rates, reduced latency and improved spectral efficiency and thus deliver superior voice quality and overall network performance. Various recent research works has begun to investigate the comparative per- formance of VoNR and Voice over LTE (VoLTE) with specific focus on energy consumptions. Most of the work were related to power optimization, however sig- nificant part of all the previous studies oriented towards network side optimization. The previous works were mostly laboratory experiments, simulations, or mathemat- ical models [1], [2], [3]. As per our literature review, no major work is done for the smartphone battery optimization in the real world scenario during VoNR mobility calls. Building on this foundation, our research aims to address this gap by conduct- ing the experiment to evaluate the smartphone battery performance during mobility when VoNR and VoLTE voice calls ongoing. We propose an experimental methodology designed to concurrently measure smart-phone battery consumption within a live 5G SA network. By integrating technical parameters with an assessment of the estimated user experience, this study seeks to provide a more understanding of the trade-offs between energy efficiency and voice quality in next-generation mobile networks. Our study presents an experimental evaluation conducted within a live 5G Stan- dalone(SA) network. We performed drive testing with a smartphone engaged in an active VoNR & VoLTE calls, during which we measured key performance indicators, including battery discharge and the number of network handovers. This methodol- ogy allows us to offer a holistic assessment of the end-user experience in relation to the technological efficiency of the 5G mobile network ecosystem. A key aspect of this ecosystem is the interworking with 4G networks, where a coverage-based handover is initiated when a User Equipment (UE) moves into an area with poor New Radio (NR) signal. While laboratory setups offer the advantages of reduced measurement noise and simplified power collection, they provide an artificial environment. Performance benchmarking conducted in a lab, though stable, often fails to reflect real-world con- ditions. In contrast, our approach, which utilizes a live air network, establishes a more realistic foundation for performance benchmarking and the development of accurate predictive models. This work is divided in the following sections: II : Related works, limitations and differences with this research. III : Key concepts and backgrounds considered in this work. IV : Methodology process and configuration. V : Presentation and analysis of findings. VI : Summary of results, insights, and areas for future investigation. 2 Related works The global adoption of 5G networks has significantly transformed voice commu- nication over mobile broadband. As Mobile Network Operators (MNOs) undergo transition from Long Term Evolution (LTE) to New Radio (NR), understanding the trade-offs between these technologies-particularly concerning smartphone power consumption-has become essential. We studied various aspects of the power saving in the smartphones and observed three major criteria for the same. Firstly, the studies are focused on the UE side optimizations. Secondly, the voice calls related optimiza- tion (VoNR, VoLTE calls) are researched. Thirdly, the various work which is done by researchers regarding the handover optimizations and power conservation mechanism therein. While a substantial body of recent work [ 4 ], [ 5 ], [ 6 ], [ 7 ], [ 8 ], [ 9 ], [ 10 ], [ 11 ] has explored various aspects like paging, handovers, carrier aggregation, channel measure- ments, DRX related, PEI related, or paging aspects, few studies have jointly evaluated them from the User Equipment (UE) perspective. For instance, some research has measured the battery consumption of 4G Voice over LTE (VoLTE) and Voice over New Radio (VoNR) in controlled environments [ 12 ], but these studies lack deployment in live networks. Concurrently, other studies have emphasized voice quality, comparing estimated Mean Opinion Score (MOS) values and jitter patterns between 4G VoLTE and VoNR. These works often argue that VoNR outperforms 4G VoLTE in main- taining voice clarity under network impairments such as packet loss or delay [ 13 ]. Nevertheless, these analyses frequently disregard the implications of energy consump- tion, a critical factor for battery-powered devices, especially during prolonged active calls or in areas with constrained base station coverage. This work addresses these gaps by jointly measuring the battery consumption of VoNR and VoLTE calls during mobility within a live 5G Standalone (SA) network. Through this approach, we compare technological efficiency and provide a holistic view of the user experience Our research contributes novel insights to the ongoing optimization of voice services within 5G mobile network ecosystems. For a comparative summary of related work, including their primary contributions, limitations and key differences, please refer to Table-1. As far as VoNR work is related, not much prior arts exists. Moreover, all the previous work is carried our either in controlled lab environment or a simulation, which lack real world environment. Hence to know the real world impact on the smartphone battery performance it was natural to conduct this experiment to understand and gather the first hand information from the real 5G SA MNO. Table 1 Comparison with UE Specific Power Optimization papers Ref Objective Methodology Contribution Limitation [ 4 ] UE wakes up for paging occasions (PO) which con- sumes excess power. Two types of PO monitoring proposed. In the first type, the UE monitors short messages as well as paging messages, while in the second type, the UE will monitor only short message. 37% more power saving Paging optimization not required in RRC connected procedure (In our experi- ment, UE is in RRC con- nected mode always) [ 5 ] Handover challenges dur- ing high velocity move- ment of the UE. Fuzzy logic system in various logical conditions like signal strength, speed and cell load Velocity-aware fuzzy logic- based HO optimization algorithm (VAFL). Algorithm faces challenges during on-device deploy- ment. [ 6 ] Time sharing mechanism for CA bands in Dual-SIM UEs, if CA band combo not supported by NW. Transmit serving CA band of SIM-2, prepares the list compatible CA band (of SIM-1) and send to NW-1. Throughput improvement approx. 36% in mobil- ity scenario under varying DRX cycle length. Solution is MATLAB sim- ulation and not real world deployment, unlike our experiment which is per- formed in the live NW. [ 7 ] Challenge for channel mea- surements and dynamic beam in higher spectrum. Considers UE’s probabilistic transi- tions through different power con- sumption states. 35% power savings com- pared to the 3GPP legacy DRX scheme. Solution is a simulation, unlike our work. [ 8 ] IDRX and PEI have not been fully equipped for the paging characteristics of 5G-A downlink services. LSTM-FNN neural network to predict the arrival time of future paging mes- sages using historical real data. Power savings of up to 38.89% while maintaining effective latency control Paging optimization not required in RRC connected procedure. [ 9 ] UE assistance Information (UAI) based Traffic Classi- fication approach Classified the traffic at the UE and configured as per the needs of the cur- rent application. The UE power consump- tion reduced upto 60% and 50% in the cell-center and cell-edge area respectively. Mobility Scenario not cov- ered. [ 10 ] Paging optimization is pro- posed, as Predictive-PEI (PPEI) Group of end user made, based on their mobility patterns. Then predicts the current gNB of user and the expected new gNB basis KNN model. 86% efficient probabilistic method. using the paging Its a MATLAB simulation, where energy conversation is not the target, it reduces signaling overhead of the NW. [ 11 ] In the mmW communica- tion, the paging informa- tion cannot be broadcasted simultaneously over all the beams. UE indicates its presence in the gNB identified TX beam by transmitting BPI (Beam Presence Indicator) before PO. Authors has performed the MATLAB simulation for the proposed work Mobility explored. aspect is not This Work Evaluate Battery Perfor- mance for VoNR and 5G VoLTE calls during Mobil- ity Experiment conducted in live 5G SA network across major chipsets. A detailed analysis of battery power consump- tion during VoNR and 5G VoLTE call during mobility in real network environment Do a deep analysis of the data and the statis- tical approaches for bet- ter understanding of MNO and end user scenarios Cross-slot scheduling in 5G New Radio (NR) is a technique to enhance resource allocation efficiency by allowing scheduling of user equipment (UE) resources across different time slots within a frame, rather than confining scheduling to a single slot. This approach enables more flexible and dynamic resource allocation. Using this, the NW performance is improved and latency is reduced. Cross-slot scheduling can better accommodate varying traffic patterns and optimize the use of available bandwidth. In the paper [ 14 ], the authors examines cross-slot scheduling effect during Voice over New Radio (VoNR) calls and high data throughput scenarios. The study was conducted using measurements on mobile devices using scenarios created by Rohde & Schwarz CMX500 5G test system. In this study, the researchers have introduced different delays between the Physical Downlink Control Channel (PDCCH) and the Physical Downlink Shared Channel (PDSCH). The results in this study displays reductions in battery consumptions, however the same approach is valid upto specific points. Beyond a specific points, the same approach shows diminishing results. Real world testing across various NW conditions and device modes would provide more realistic insights for this features’ benefits and limitations. In another work [ 1 ] related to power consumption of voice call in SA and well as LTE NW, the test bed is used to simulate virtualized 4G core and 5G core NW as well as IMS NW. Power monitor equipment is used to measure the external power of the smartphone. Overall, this study concludes that during the experiment, the DUT consumes 38.88% less energy while making voice calls using 5G SA as compared to 4G. Existing VoNR selection procedures, defined by 3GPP, rely mainly on signal power levels and priority criteria from the network’s system information. The paper [ 15 ] proposes a novel protocol and algorithm that allows the UE to maintain a history of the availability of features like Semi-Persistent Scheduling (SPS), Connected Mode Discontinuous Reception (CDRX), and VoNR support in the serving cell’s area. This historical information is then used during systemn reselection evaluations, particularly when the UE is mobile. This work is a proposed theory and actual simulation or realisation is yet to be done. In 5G Standalone (SA) network, ”N1 mode supported” indicates that the network is capable of operating in a configuration where the 5G NR (New Radio) interface is used for both control signalling and user data, without relying on the LTE network. This means that the 5G SA network can fully manage all aspects of communication, including mobility. session management, and data transmission, entirely within the 5G infrastructure. 3GPP TS 24.501, Section 4.3.2 (Domain selection for UE originating sessions / calls) mentions about when UE shall disable the N1 mode capability for 3GPP access. VoNR call is not a good option for voice services when NR serving cell or the neighbouring NR cells are below a certain threshold. This work [ 16 ] relates to network procedures performed by a UE for managing a Voice over New Radio (VoNR) call. The method includes disabling Radio Resource Control (RRC) ”Voice Over NR” capability bit to FALSE upon determining the signal strength of the 5G NR serving cell is less than a threshold value. Hence, under such condition, an Evolved Packet System (EPS) fallback process to establish the VoNR call over the 4G cell as a VoLTE call. The call setup time is also an important criteria in the cellular communication for the end user experience and the user equipment battery optimizations. In the work [ 17 ], under the controlled lab environment the findings confirms that VoNR is superior when compared to EPS Fall Back (EPSFB) in the faster call setup time. Various 3GPP releases were compared for the same in this work, although Rel-16 devices shows better call setup time when compared to previous releases. Given that the handover process is a critical operation during smartphone mobil- ity, and the continuity of Voice over New Radio (VoNR) calls is heavily dependent on its efficiency, a detailed literature review also conducted to investigate various optimization strategies for handovers in mobility scenarios, as discussed further. The seamless continuity of communication during handover (HO) procedures is a critical requirement for user experience in next-generation mobile networks. While significant research has been dedicated to HO optimization at the network level, the direct deployment of these solutions at the smartphone level presents considerable challenges. We examine various approaches, including conditional handover (CHO) enhancement, phase-specific delay analysis, context-aware strategies for vehicular net- works, and machine learning (ML)-based predictive methods. The discussion highlights the tension between network-centric control and UE-based autonomy, the computa- tional complexities associated with advanced algorithms, and the prevalent reliance on simulation-based validation, which often leaves the practical feasibility for smart- phone deployment an open question. Our review related to handover optimizations aims to provide a clear understanding of the current landscape, identifying promising research directions and the existing gaps in achieving efficient, UE-deployable HO solu- tions. Much of the existing literature focuses on HO optimization from the network’s perspective, where operators have prerogative control over HO decisions. However, implementing such controls directly on smartphones is often not feasible, as the HO logic is typically governed by the network infrastructure. Conditional Handover (CHO) represents an evolution from traditional HO mecha- nisms by introducing more detailed criteria for initiating a cell switch. Unlike normal HO, which is typically triggered by simple thresholds such as signal strength (e.g., when the serving cell’s signal quality degrades below a certain level and a neighbor- ing cell offers a stronger signal), CHO incorporates a broader set of conditions. These can include network congestion levels, specific user service requirements, or other per- formance metrics, aiming to make HO decisions more intelligent and context-aware. A study presented in [ 18 ] contributes to this area by proposing an optimization for the CHO process. The UE is configured with CHO parameters for ‘n‘ potential target cells, along with their respective feature values. The UE then continuously monitors all these CHO candidates. From this pool, it selects a subset of ‘m‘ candidates (denoted as C1, C2,. . ., Cm) that demonstrate better measurement results than the current serving cell. A Q-learning algorithm is subsequently applied to this refined list of can- didates to make an informed and optimized HO decision. This approach aims to make the CHO process more adaptive and intelligent by leveraging reinforcement learning to evaluate and select the most suitable target cell under dynamic network conditions. The efficiency of the HO process is often evaluated by analyzing the delays asso- ciated with its distinct phases. The work in [ 19 ] provides a detailed breakdown of these phases, categorizing them into preparation, execution, and completion. A key finding of this analysis is the significant variation in HO preparation delay between Non-Standalone (NSA) and Standalone (SA) 5G network architectures. In SA net- works, where the next-generation NodeB (gNB) operates independently, HO decisions based on UE measurement reports are transferred directly from the serving gNB to the target gNB. This direct communication path contributes to a relatively streamlined preparation phase. In contrast, NSA networks, which rely on an Evolved Universal Ter- restrial Radio Access Network (E-UTRAN) New Radio-Dual Connectivity (EN-DC) architecture, involve a master node (typically an LTE eNodeB) that intermediates the communication between the serving and target gNBs. This indirect path introduces additional latency, making the HO preparation delay more pronounced in NSA con- figurations compared to SA networks. The study quantifies this, noting that the HO preparation delay constitutes nearly 40% of the total HO time across various loca- tions and population densities. Furthermore, the execution phase of the HO is also scrutinized. A major contributor to delay during this phase is the downlink chan- nel synchronization time (Tsync), which is the time required for the UE to achieve downlink synchronization with the target gNB. On average, Tsync also accounts for approximately 40% of the HO execution delay, highlighting it as a critical area for optimization to reduce overall service interruption. The unique challenges of maintaining connectivity for high-speed users, particu- larly Connected Autonomous Vehicles (CAVs), have spurred dedicated research. In [ 20 ], a novel method tailored for millimeter-wave (mmWave) environments is proposed to reduce HO frequency. The Vehicular Frequency Reuse (VFR) scheme is central to this work. It distinguishes between low-speed and high-speed users by employing a sim- ple scalar metric known as the Velocity-Threshold (VT). Based on this threshold, users are served with different sets of communication channels. The VT value is not static but is adaptively calculated using the K-means machine learning algorithm. This algo- rithm clusters reported vehicle velocities to infer underlying road conditions, allowing the VT to dynamically adjust to the current traffic environment. The methodology was validated through extensive computer simulations. The results highlight that the VFR scheme effectively reduces air interface traffic load and significantly decreases HO rates. This dual reduction contributes to smoother and more efficient operation for the UE, which is particularly crucial for latency-sensitive vehicular applications. Radio Link Failure (RLF) is a primary cause of Handover Failure (HOF), lead- ing to extended service interruptions that severely impact the Quality of Experience (QoE) for users in 5G New Radio (NR) networks. To address this, the work in [ 21 ] pro- poses a proactive HO strategy designed to initiate the handover process before the UE encounters an RLF, thereby minimizing HOF occurrences. The solution leverages a novel Machine Learning (ML) and beam measurement-based advanced HO algorithm. The ML model is trained using a set of network parameters derived from the UE’s serving cell, including Reference Signal Received Power (RSRP), Block Error Rate (BLER), Timing Advance (TA), and the direction of the serving beam. By analyz- ing these parameters, the algorithm can predict potential RLF conditions and trigger the HO in advance. The study specifically employs the K-Nearest Neighbors (KNN) algorithm for this predictive task. The overarching goal is to ensure minimal interrup- tion time and a high handover success rate, which are fundamental to maintaining a superior QoE in 5G NR systems. A significant body of research explores the use of various ML algorithms to predict user mobility patterns, with the aim of optimizing HO decisions based on these pre- dictions. During our literature review, we found that these mechanisms hold promise for enhancing HO efficiency, they often introduce high computational costs at the UE. This raises a critical trade-off: the potential benefits of optimized HO must be weighed against the increased energy consumption, which may negate any battery savings. Fur- thermore, a large portion of these works are validated through simulations, leaving the actual performance and feasibility of deployment on commercial smartphones an area requiring further investigation. One such approach, proposed in [ 22 ], introduces a hybrid model for user mobility prediction in heterogeneous networks, where users transition between different types of base stations (e.g., macro cells and small cells). The method combines Markov Chain models, which are effective for modeling state transitions, with Artificial Neural Networks (ANNs), which can capture complex, non-linear patterns in user movement. This hybrid approach aims to improve the accuracy of mobility predictions, thereby enhancing overall HO management in diverse network environments. Similarly, the work in [ 23 ] presents an intelligent HO decision-making framework specifically designed for Vehicle-to-Everything (V2X) networks in 5G environments. This framework utilizes machine learning optimizations to predict vehicle mobility and network conditions proactively. By doing so, it aims to improve HO efficiency, mini- mize latency, reduce dropped connections, and optimize resource allocation, ensuring seamless connectivity for critical V2X applications. The framework’s performance was validated through simulation studies, demonstrating its potential benefits. In a different vein, the research in [ 24 ] focuses on mitigating frequent handovers in dense small-cell networks, a common problem due to the smaller coverage areas of these cells. The proposed HO procedure is based on mobility prediction, where the sys- tem anticipates user movement patterns. By forecasting these trajectories, the method can reduce unnecessary handovers, thereby improving network efficiency and the user experience. The authors emphasize that this is not a network-side optimization sim- ulation but rather a UE-centric approach. The reported results indicate a significant reduction in HO frequency, showcasing the potential of mobility prediction techniques in managing the complexities of small-cell deployments. Future research should prioritize the development of computationally efficient algo- rithms, real-world testing and validation, and a closer examination of the network-UE control paradigm to enable more intelligent, user-centric handover management that aligns with the demands of future mobile applications. 3 Key Concepts 5G Standalone (SA) and 5G Non-Standalone (NSA) represent two distinct architec- tural approaches for deploying 5G services, each with unique implications for energy consumption. 5G SA is a pure 5G deployment where the User Equipment (UE) con- nects directly to a 5G Core (5GC) network via the 5G New Radio (NR) interface. Active call control and media anchoring occur entirely within the 5G domain. The call flow for VoNR is depicted in Fig-1. This architecture eliminates dependence on the legacy LTE Evolved Packet Core (EPC). In this mode, Voice over New Radio (VoNR) becomes the native voice solution, managed entirely within the 5G infras- tructure. It is anticipated that VoNR will offer lower jitter and faster call setup times due to the enhanced NR radio interface and the use of advanced codecs such as the Enhanced Voice Service (EVS). Furthermore energy consumption is expected to be lower than that of Voice over LTE (VoLTE), as SA allows for more efficient radio resource utilization and incorporates advanced power-saving mechanisms. The IP Multimedia Subsystem (IMS) [25] is a standardized architecture developed by the 3rd-Generation Partnership Project (3GPP) for delivering multimedia services- for instance, voice, video, and messaging-over IP networks. Although initially designed for 3G networks, IMS has evolved to support 5G VoNR and VoLTE voice services, enabling interoperability session control, and Quality of Service (QoS) across various access networks, including LTE, Wi-Fi, and NR. During the initial stages of VoNR commercial deployment, several factors are con- sidered. Given that users often perceive voice services as more critical than data services, and in light of ongoing 5G coverage challenges and network optimization efforts, VoNR is not enabled across the entire network simultaneously. Instead, it is deployed in phases, initially in areas where the network meets stringent VoNR qual- ity requirements. Regions with mature 5G optimization will see commercial VoNR deployment first. In other areas, the VoNR function may remain deactivated, and the Evolved Packet System (EPS) Fallback method will continue to be used for voice calls. This phased approach results in the emergence of VoNR functional switch boundaries [27]. The various radio frequency bands acquired by telecom operators in India to date are summarized in Fig-2. Each band offers unique-benefits, and it is incumbent upon the operator to maximize its use for specific applications. Generally, the n28 band provides superior penetration characteristics, making it suitable for enhanced indoor coverage, while the n78 band is advantageous for broader outdoor coverage. Accord- ing to some reports, the n78 band can interfere with aircraft altimeters, leading to restrictions on its deployment in airport areas, Consequently, operators must utilize other NR bands in such locations. Lower frequency bands (Frequency Range 1, FR1) typically offer better coverage and penetration through obstacles but provide limited bandwidth. Conversely, higher frequency bands (Frequency Range 2, FR2) deliver larger bandwidths, enabling higher data rates, but suffer from limited coverage and are more susceptible to signal attenuation. Moreover, due to support for multiple antennas and more powerful radio frequency modules in 5G NR, 5G devices tend to generate more heat than their LTE predeces- sors. This introduces new thermal management challenges for wireless devices. 3GPP Release 16 has introduced suggestions to mitigate this issue, such as allowing devices to latch onto the 4G radio from the 5G radio when they reach a specific temperature threshold. In addition to these standards, Original Equipment Manufacturers (OEMs) have also implemented device-specific optimizations to address thermal management. Physical Cell Identify (PCI) also plays a major role in the VoNR call signaling. In the 5G Network, the PCI change is associated with the cell reselection or redirection. However, if the PCI change involves switch to different cell within same NW, it is interpreted as handover-like process, especially during call continuity process. During RRC connected state, the transition in the serving cell identity for the user equipment signifies a handover (HO), which is essential for transferring an active communication session from one cell to another, ensuring seamless connectivity as user moves across NW. To simulate real-world conditions, a continuous data download was maintained in the background throughout our experiment. 4 Methodology 4.1 Setup Configuration Our experiment was conducted in a real world environment within the Noida area of Delhi NCR, India, as illustrated in Fig-3. The setup utilized both 4G and 5G Stan- dalone (SA) core network functions from a single Mobile Network Operator (MNO). The Devices Under Test (DUTs) were equipped with 5G SA SIM cards in the first SIM slot. Each DUT ran the Android 15 operating system, and all third-party appli- cations were uninstalled to prevent any background data activity from influencing the results. To monitor network parameters and count handovers, the G NetTack Lite’ application [29] was installed on all DUTs. This dedicated software facilitates real- time monitoring of key metrics during drive testing, including time, serving Cell ID, RSRP, RSSI, speed, and Radio Access Technology (RAT) type. A screenshot of the application interface is provided in Fig-4. Each Voice over New Radio (VoNR) mobile originated (MO) call was intended to last for 10 minutes. To evaluate battery performance during the drive tests, the battery level displayed on the DUT’s user interface was recorded at 10-minute intervals. If a VoNR call experienced a fallback to LTE (Evolved Packet System Fallback, EPSFB), the call was terminated, and the system would wait for 5G SA network availability before placing the next VoNR call. The primary objective was to assess the impact of the VoNR and VoLTE calls in 5G SA network on DUT battery consumption. The frequency bands utilized during the experiment are detailed in Table 2 . The drive test vehicle maintained a speed between 60 and 80 kilometers per hour (KMPH), a typical speed for traffic in the test area. Table 2 Live 5G SA Network Configuration used in the experiment NR Bands (5G SA) n78, n28 LTE Bands B3, B5, B40 NRCA n78 n28 4.2 Device Under Test (DUT) The device under test (DUTs) is an advanced mobile device as shown in Table-3. Specifications can also be referred at GSM Arena website [30]. Table 3 DUT Specifications Spec DUT-1, DUT-2 DUT-3, DUT-4 DUT-5, DUT-6 Chipset Qualcomm SM8650-AC Snap- dragon 8 Gen Exynos 2400 (4 nm) MediatekRimen- sity, 6300 (6 nm) Model Name Samsung S24 Ultra Samsung S24 Samsung A16 Battery Li-Ion 5000 mAh Li-Ion 4000 mAh Li-Ion 5000 mAh Display 6.8 inches 6.2 inches 6.7 inches Resolution 1440 x 3120 pixels 1080 x 2340 pixels 1080 x 2340 pixels GPU Adreno750(1 GH2) Xcipse 940 Mali-G57 MC2 Operating System Android 15 4.3 Testing Methodology The battery consumption analysis followed a structured procedure. Initially, each DUT was fully charged to 100% and configured with a 5G SA SIM card in the first slot, while the second slot remained empty. All devices were running the Android 15 software. The drive test vehicle travelled at a speed of 60–80 KMPH within the Noida NCR region, as per drive test location. We prepared one pair of devices for each of the three chipset types under examination: Qualcomm (QC), Exynos, and MediaTek, resulting in a total of six DUTs. For each chipset pair, VoNR was enabled on one device and disabled on the other, as depicted in Fig-5. During mobility, a VoNR MO call was initiated on the first device of the pair, with the goal of maintaining a continuous connection for 10 minutes. Simultaneously, on the second device (where VoNR was disabled), a VoLTE call was triggered. Furthermore, a data download session from an FTP server was initiated on all DUTs, running in parallel with the active voice calls. This concurrent data activity was designed to keep the VoLTE device in a Radio Resource Control (RRC) connected state throughout the mobility test, ensuring a consistent and comparable testing scenario across all devices. To isolate the impact of voice services and data activity on battery consump- tion, other hardware components and software features- such as Bluetooth, GPS, accelerometers, Wi-Fi, NFC, and native Android applications (e.g., Gmail, Chrome)- were disabled. A total of two hours of drive testing was performed for each device pair. Throughout these tests, the mobile-terminated (MT) side of the call was main- tained at a stationary location approximately 10 kilometers away. This setup helped mitigate the risk of call drops attributable to failures at the MT end, such as Radio Link Failure (RLF), handover failure, or ping-pong effects. The overall experimental methodology was executed in several distinct stages, as outlined in Fig-6. In total, the experiment was conducted for total 6 hours for both 5G VoNR and VoLTE technologies, encompassing 6 DUTs and collecting approx 90 data samples. The decision to conduct the experiment in a live, open-air environment was intentional and methodologically justified. This approach provides authentic network conditions, contrasting with the controlled, and often non- representative, settings of laboratory experiments or simulations. While laboratory environments can reduce measurement noise and simplify instrumentation, they frequently fail to capture the significant variations observed between theoretical measurements and real-world performance. 5 Results This paper presents an analysis of battery power consumption and handover fre- quency in 5G networks utilizing the IP Multimedia Subsystem (IMS), across various smartphone chipsets. A comparative evaluation is conducted between 5G Standalone (SA) and 4G LTE network architectures to demonstrate the corresponding impact on battery power consumption. Fig-7, Fig-8, Fig-9 illustrates the total battery consumption during a drive test of 120 minutes approximately, encompassing three distinct chipsets: Qualcomm (QC), Exynos, and MediaTek (MTK). Throughout this drive test, the Device Under Test (DUT) transitioned between congested and decongested network environments, exe- cuting multiple handovers and Evolved Packet System Fallback (EPSFB) procedures. The findings indicate that in mobility scenarios, Voice over New Radio (VoNR) calls within a 5G SA network exhibit lower energy efficiency compared to Voice over LTE (VoLTE) calls. These results hold considerable significance for Original Equipment Manufacturers (OEMs) and Mobile Network Operators (MNOs) aiming to optimize smartphone energy efficiency and prolong battery life. VoLTE calls during the mobility scenario demonstrate the superior battery performance in contrast to the VoNR calls. This disparity is power utilization shows the complexity of maintaining voice calls over 5G networks, which require more resources to ensure call quality and stability. As illustrated in Fig-7, the Qualcomm device exhibited 63% battery remaining after 110 minutes of Voice over New Radio (VoNR) calling. In comparison, a Voice over LTE (VoLTE) call of 118 minutes resulted in 66% remaining battery capacity. This data suggests that VoLTE technology demonstrates approximately 3% greater power efficiency under mobility conditions. It is important to note that practical con- straints during drive testing prevented the precise equalization of VoNR and VoLTE call durations. To ensure a robust analysis, the VoLTE call duration was intention- ally extended slightly beyond that of the VoNR call. A similar trend was observed in devices equipped with Exynos chipsets. Following 96 minutes of VoNR usage, the battery percentage was 57%, whereas a 110-minute VoLTE call maintained 60% bat- tery charge. This again points to a marginal efficiency advantage of approximately 3% for VoLTE over VoNR. Conversely, MediaTek devices displayed a more pronounced difference in battery consumption between the two calling technologies. For identical 118-minute call durations, VoNR and VoLTE usage resulted in 59% and 72% remain- ing battery power, respectively. This represents a significantly larger efficiency gap for VoLTE compared to VoNR than was observed in the Qualcomm and Exynos device tests. Similar to the VoNR and VoLTE call in the 5G SA NW, handover events were also evaluated during our experiment under the same scenario of the drive testing for each chipset. The test result shows that HO counts contributes no significant impact of the smartphone battery performance across chipsets. However, at the same time, we observed that the HO count is more in VoLTE calls in comparison to VoNR calls. This observation is noteworthy, as lower HO is anticipated during the VoNR calls, as per the theoretical research as well. This is directly attributed to the practical implications of the NW planning, optimization techniques and resource allocations. An examination of Fig-10 reveals that Voice over New Radio (VoNR) calls exhibited the lowest HandOver (HO) count in Exynos devices, while the highest HO count was observed in MediaTek devices. Conversely, during Voice over LTE (VoLTE) calls, the HO count was highest in Qualcomm devices and lowest in those equipped with Exynos chipsets. Fundamentally, the data indicates that Exynos devices consistently demonstrate the lowest HO counts for both VoNR and VoLTE technologies. This trend suggests the presence of device-specific optimizations, potentially implemented by the Original Equipment Manufacturer (OEM), to enhance performance during mobility scenarios. In our experiment, wherein we evaluated the smartphone battery performance dur- ing 5G and 4G voice calls in mobility, the call sustainability is also on important aspect. During our testing we observed that VoNR call failed to maintain the connectivity for the full 10 minutes test duration in all the evaluated chipsets. During the testing, the EPSFB was frequently triggered to maintain the call continuity. This fallback to VoLTE preserved the active calls. Moreover, we observed that VoLTE calls has shown seamless continuity and this exhibit superior robustness in the dynamic environment. The analysis of battery depletion characteristics during mobility revealed a con- sistent trend across all tested chipsets, as illustrated in Fig-11. Irrespective of the chipset manufacturer- namely Qualcomm (QC), Samsung LSI (SLSI), or MediaTek (MTK)–the rate of battery depletion followed a remarkably similar pattern. This uni- formity suggests that the underlying technological framework and prevailing network conditions exert a more significant influence on battery performance than the specific chipset employed during VoNR and VoLTE call. Handover efficiency represents another domain where distinct behavioral dif- ferences between VoNR and VoLTE were observed. Drive test results indicated a significantly lower number of handovers (HO) when the VoNR feature was enabled on the device, compared to when it was disabled. This reduction in HO frequency suggests that VoNR mode may employ enhanced network resource management strate- gies, potentially leading to fewer inter-cell transitions. However, this optimization in handover efficiency is accompanied by increased power consumption, a phenomenon noted in the preceding analysis. Furthermore, the impact of varying radio conditions on battery performance was investigated across diverse environments, including expressways, commercial market areas, and congested urban zones. The drive test, covering a distance of approximately 20 kilometers, encompassed these varied radio propagation environments. Despite the heterogeneity in radio conditions, no significant impact on battery performance was observed. The consistency in battery depletion trends across these environments rein- forces the conclusion that network conditions are not a primary determinant of battery efficiency for VoNR and VoLTE calls during mobility. In conclusion, this comparative analysis of drive test data from devices equipped with QC, SLSI, and MTK chipsets highlights key performance differentials between VoNR and VoLTE technologies The study underscores significant differences in battery consumption, call sustainability, handover efficiency, and resilience to fluctuating radio conditions. While VoNR presents potential advantages in network resource optimiza- tion, its higher power consumption and current limitations in sustaining calls during mobility represent substantial challenges. Conversely VoLTE demonstrates greater sta- bility and power efficiency in dynamic scenarios, positioning it as the more reliable solution for voice communication in mobile contexts. As 5G infras, tructure continues to mature, further technological advancements in VoNR will be essential to reconcile the performance gap with VoLTE particularly concerning power efficiency and seamless handover management. These results also suggests that the performance of 5G SA VoNR call isn’t entirely independent of the activity on the 4G core and VoLTE network. MNOs can improve 5G SA performance by optimizing resource allocation strategies considering inter- connection with the 4G core network activity. The fact that VoNR activity influences 5G SA performance highlights the importance of considering cross-technology interac- tions when designing and operating multi-generation networks. VoNR calls has impact on the energy consumption and accounts considerable for 5G SA NW. 6 Discussion This section delves into the experimental results, offering contextual analysis and exploring their implications for future 5G network deployments and ongoing research. Multiple real-world factors within the 5G network (NW) ecosystem significantly influ- ence the battery performance of voice services. Parameters such as packet delays, retransmissions, signal quality, and frequent cell reselections or handovers directly affect the processing cycles and radio activity of the Device Under Test (DUT). In the current experiment, conditions were held constant for both Voice over New Radio (VoNR) and Voice over LTE (VoLTE) calls, ensuring parameter parity across both scenarios. However, these dynamic conditions are challenging to fully replicate in a laboratory environment, which typically benefits from stable radio frequency (RF) conditions and a static network topology. Consequently, field deployments may exhibit greater energy variability due to these fluctuating environmental factors, as evidenced by this work. The findings of this study are derived from observations in a live network, The 5G Standalone (SA) and 4G LTE networks have been operational since 2022 and 2016, respectively [31]. The VoNR service is still in the process of being deployed [32], and it is crucial to note that during this deployment phase on the SA network, smartphone power consumption for VoNR calls is higher compared to VoLTE services on the 4G network of the same Mobile Network Operator (MNO). While both technologies deliver high voice quality in realistic environments, VoLTE consistently demonstrates superior power efficiency acrosst various chipsets. The observed variance in 5G VoNR performance scores may indicate network instability, potentially leading to increased DUT battery consumption. This suggests that while the SA architecture offers initial advantages in speed and latency, VoLTE can still achieve better power conservation under comparable network conditions for voice calls. The overall results from our experiment shows that VoNR and VoLTE call in the mobility is influenced by a complex factors and not just the direct energy metrics. The other paraments which may affect the call continuity are NW radio conditions, radio resources’, allocation methodologies and codec selection. It’s important to note that the end user is not usually aware of these technical parameters. It’s also important to note that in this experiment various 3rd party app and background data activity were restricted, however they may have direct impact on the battery optimizations as well. Future Research Directions: Several promising avenues for future research emerge from this work, as follows: Cross-OEM Comparative Analysis: Undertake a comparative study of VoNR and VoLTE call performance across multiple Original Equipment Manufacturers (OEMs) to understand better about the OEM specific optimization can also affect battery performance. Software Optimization: A test software build to check the impact of the velocity on the battery performance under varying speed during VoNR call. If the smartphone is prone to higher speed above threshold, the VoLTE can shall be preferred over VoNR to save the battery. Dual SIM Impact: The impact of the dual SIM and the related performance in the live NW during mobility would be very interesting area to explore. 7 Conclusion The widespread deployment of fifth-generation (5G) networks marks a significant milestone in mobile communications, enabling more efficient and dedicated voice packet transmission. This advancement is crucial for supporting real-time services, particularly Voice over New Radio (VoNR), which is the native voice solution for fully operational 5G Stand-Alone (SA) networks. By leveraging the full capabili- ties of 5G—such as enhanced data rates, reduced latency, and improved spectral efficiency—VoNR promises to deliver superior voice quality and overall network per- formance. Recent research has begun to investigate the comparative performance of VoNR and its 4G predecessor, Voice over LTE (VoLTE), with a particular focus on smartphone energy consumption. Notably, studies indicate that during mobile scenar- ios, VoNR can significantly reduce battery drain compared to VoLTE. Building on this foundation, our research aims to address a critical knowledge gap by conducting a comprehensive evaluation of both 5G VoLTE and VoNR services. We propose an experimental methodology designed to concurrently measure smartphone battery con- sumption within a live 5G SA network. By integrating objective technical parameters with an assessment of the estimated user experience, this study seeks to provide a more constructive understanding of the trade-offs between energy efficiency and voice qual- ity in next-generation mobile networks. Our observations revealed that battery drain was significantly higher during active VoNR calls in mobile scenarios compared to VoLTE calls. This finding contradicts results from previous laboratory-based studies, which suggested different performance characteristics. This study highlights the critical importance of device-side optimization strate- gies in addressing the battery consumption challenges posed by 5G NR technology. By focusing on practical, implementable solutions at the smartphone level, our research contributes to the development of more sustainable and efficient 5G mobile communication systems. 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8","display":"","copyAsset":false,"role":"figure","size":68031,"visible":true,"origin":"","legend":"\u003cp\u003eExynos Device Battery Performance\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7754087/v1/7f6b942cd93ba88b83a7ded2.png"},{"id":92579304,"identity":"841824c5-38ee-4017-9cdb-4f35f0b8c27d","added_by":"auto","created_at":"2025-10-01 09:01:36","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":71941,"visible":true,"origin":"","legend":"\u003cp\u003eMTK Device Battery Performance\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7754087/v1/b452f707be8709616ffaf243.png"},{"id":92580010,"identity":"6d30592c-3e7d-48c8-ae95-1a124c5a79a2","added_by":"auto","created_at":"2025-10-01 09:09:35","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":70254,"visible":true,"origin":"","legend":"\u003cp\u003eHandover Count during Drive Test\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-7754087/v1/0013d765b263404c5f21a1a2.png"},{"id":92579285,"identity":"ba3b5e83-b78d-4df8-80fa-896a97272b95","added_by":"auto","created_at":"2025-10-01 09:01:35","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":109127,"visible":true,"origin":"","legend":"\u003cp\u003eBattery performance across chipsets\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-7754087/v1/ebcc276c904723fce0f3b1a2.png"},{"id":92921833,"identity":"29d16ac2-ead3-4ec6-b2ee-4f3f347a72fd","added_by":"auto","created_at":"2025-10-07 07:09:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2636716,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7754087/v1/2d548e98-505c-4e15-8789-8172e7774cd8.pdf"},{"id":92579268,"identity":"3ee7a519-eed7-48d7-ad1c-fff54085b883","added_by":"auto","created_at":"2025-10-01 09:01:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":246970,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFileforDetailedObservation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7754087/v1/b6656ac88b9d13da4f50e90e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Experimental Insight on the Impact of VoNR and VoLTE Calls on the Smartphone Battery Performance during Mobility in 5G SA Network","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003e5G New Radio communication is major milestone in mobile communication which has enables the efficient and dedicated voice packet transmission. Voice over New Radio (VoNR) in particular is crucial, which is native voice service in 5G Standalone (SA) Network. VoNR utilizes the full capacity of 5G NR SA network such as enhanced data rates, reduced latency and improved spectral efficiency and thus deliver superior voice quality and overall network performance.\u003c/p\u003e\n\u003cp\u003eVarious recent research works has begun to investigate the comparative per- formance of VoNR and Voice over LTE (VoLTE) with specific focus on energy consumptions. Most of the work were related to power optimization, however sig- nificant part of all the previous studies oriented towards network side optimization. The previous works were mostly laboratory experiments, simulations, or mathemat- ical models [1], [2], [3]. As per our literature review, no major work is done for the smartphone battery optimization in the real world scenario during VoNR mobility calls. Building on this foundation, our research aims to address this gap by conduct- ing the experiment to evaluate the smartphone battery performance during mobility when VoNR and VoLTE voice calls ongoing.\u003c/p\u003e\n\u003cp\u003eWe propose an experimental methodology designed to concurrently measure smart-phone battery consumption within a live 5G SA network. By integrating technical parameters with an assessment of the estimated user experience, this study seeks to provide a more understanding of the trade-offs between energy efficiency and voice quality in next-generation mobile networks.\u003c/p\u003e\n\u003cp\u003eOur study presents an experimental evaluation conducted within a live 5G Stan- dalone(SA) network. We performed drive testing with a smartphone engaged in an active VoNR \u0026amp; VoLTE calls, during which we measured key performance indicators, including battery discharge and the number of network handovers. This methodol- ogy allows us to offer a holistic assessment of the end-user experience in relation to the technological efficiency of the 5G mobile network ecosystem. A key aspect of this ecosystem is the interworking with 4G networks, where a coverage-based handover is initiated when a User Equipment (UE) moves into an area with poor New Radio (NR) signal. While laboratory setups offer the advantages of reduced measurement noise and simplified power collection, they provide an artificial environment. Performance benchmarking conducted in a lab, though stable, often fails to reflect real-world con- ditions. In contrast, our approach, which utilizes a live air network, establishes a more realistic foundation for performance benchmarking and the development of accurate predictive models.\u003c/p\u003e\n\u003cp\u003eThis\u0026nbsp;work\u0026nbsp;is\u0026nbsp;divided\u0026nbsp;in\u0026nbsp;the\u0026nbsp;following\u0026nbsp;sections:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eII\u0026nbsp;:\u0026nbsp;Related\u0026nbsp;works,\u0026nbsp;limitations\u0026nbsp;and\u0026nbsp;differences\u0026nbsp;with\u0026nbsp;this\u0026nbsp;research.\u003c/li\u003e\n \u003cli\u003eIII\u0026nbsp;:\u0026nbsp;Key\u0026nbsp;concepts\u0026nbsp;and\u0026nbsp;backgrounds\u0026nbsp;considered\u0026nbsp;in\u0026nbsp;this\u0026nbsp;work.\u003c/li\u003e\n \u003cli\u003eIV : Methodology process and configuration.\u003c/li\u003e\n \u003cli\u003eV\u0026nbsp;: Presentation and\u0026nbsp;analysis of\u0026nbsp;findings.\u003c/li\u003e\n \u003cli\u003eVI : Summary of results, insights, and areas for future investigation.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"2 Related works","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe global adoption of 5G networks has significantly transformed voice commu- nication over mobile broadband. As Mobile Network Operators (MNOs) undergo transition from Long Term Evolution (LTE) to New Radio (NR), understanding the trade-offs between these technologies-particularly concerning smartphone power consumption-has become essential. We studied various aspects of the power saving in the smartphones and observed three major criteria for the same. Firstly, the studies are focused on the UE side optimizations. Secondly, the voice calls related optimiza- tion (VoNR, VoLTE calls) are researched. Thirdly, the various work which is done by researchers regarding the handover optimizations and power conservation mechanism therein. While a substantial body of recent work [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] has explored various aspects like paging, handovers, carrier aggregation, channel measure- ments, DRX related, PEI related, or paging aspects, few studies have jointly evaluated them from the User Equipment (UE) perspective. For instance, some research has measured the battery consumption of 4G Voice over LTE (VoLTE) and Voice over New Radio (VoNR) in controlled environments [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], but these studies lack deployment in live networks. Concurrently, other studies have emphasized voice quality, comparing estimated Mean Opinion Score (MOS) values and jitter patterns between 4G VoLTE and VoNR. These works often argue that VoNR outperforms 4G VoLTE in main- taining voice clarity under network impairments such as packet loss or delay [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Nevertheless, these analyses frequently disregard the implications of energy consump- tion, a critical factor for battery-powered devices, especially during prolonged active calls or in areas with constrained base station coverage.\u003c/p\u003e\u003cp\u003eThis work addresses these gaps by jointly measuring the battery consumption of VoNR and VoLTE calls during mobility within a live 5G Standalone (SA) network. Through this approach, we compare technological efficiency and provide a holistic view of the user experience Our research contributes novel insights to the ongoing optimization of voice services within 5G mobile network ecosystems. For a comparative summary of related work, including their primary contributions, limitations and key differences, please refer to Table-1.\u003c/p\u003e\u003cp\u003eAs far as VoNR work is related, not much prior arts exists. Moreover, all the previous work is carried our either in controlled lab environment or a simulation, which lack real world environment. Hence to know the real world impact on the smartphone battery performance it was natural to conduct this experiment to understand and gather the first hand information from the real 5G SA MNO.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison with UE Specific Power Optimization papers\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObjective\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMethodology\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eContribution\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eLimitation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUE wakes up for paging\u003c/p\u003e\u003cp\u003eoccasions (PO) which con- sumes excess power.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTwo types of PO monitoring proposed.\u003c/p\u003e\u003cp\u003eIn the first type, the UE monitors short messages as well as paging messages, while in the second type, the UE will monitor only short message.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e37% more power saving\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003ePaging optimization not\u003c/p\u003e\u003cp\u003erequired in RRC connected procedure (In our experi- ment, UE is in RRC con- nected mode always)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHandover challenges dur-\u003c/p\u003e\u003cp\u003eing high velocity move- ment of the UE.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFuzzy logic system in various logical\u003c/p\u003e\u003cp\u003econditions like signal strength, speed and cell load\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eVelocity-aware fuzzy logic-\u003c/p\u003e\u003cp\u003ebased HO optimization algorithm (VAFL).\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eAlgorithm faces challenges\u003c/p\u003e\u003cp\u003eduring on-device deploy- ment.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTime sharing mechanism\u003c/p\u003e\u003cp\u003efor CA bands in Dual-SIM UEs, if CA band combo not supported by NW.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTransmit serving CA band of SIM-2,\u003c/p\u003e\u003cp\u003eprepares the list compatible CA band (of SIM-1) and send to NW-1.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eThroughput improvement\u003c/p\u003e\u003cp\u003eapprox. 36% in mobil- ity scenario under varying DRX cycle length.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eSolution is MATLAB sim-\u003c/p\u003e\u003cp\u003eulation and not real world deployment, unlike our experiment which is per- formed in the live NW.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChallenge for channel mea-\u003c/p\u003e\u003cp\u003esurements and dynamic beam in higher spectrum.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eConsiders UE\u0026rsquo;s probabilistic transi-\u003c/p\u003e\u003cp\u003etions through different power con- sumption states.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e35% power savings com-\u003c/p\u003e\u003cp\u003epared to the 3GPP legacy DRX scheme.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eSolution is a simulation,\u003c/p\u003e\u003cp\u003eunlike our work.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIDRX and PEI have not\u003c/p\u003e\u003cp\u003ebeen fully equipped for the paging characteristics of 5G-A downlink services.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLSTM-FNN neural network to predict\u003c/p\u003e\u003cp\u003ethe arrival time of future paging mes- sages using historical real data.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003ePower savings of up to\u003c/p\u003e\u003cp\u003e38.89% while maintaining effective latency control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003ePaging optimization not\u003c/p\u003e\u003cp\u003erequired in RRC connected procedure.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUE assistance Information\u003c/p\u003e\u003cp\u003e(UAI) based Traffic Classi- fication approach\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClassified the traffic at the UE and\u003c/p\u003e\u003cp\u003econfigured as per the needs of the cur- rent application.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eThe UE power consump-\u003c/p\u003e\u003cp\u003etion reduced upto 60% and 50% in the cell-center and cell-edge area respectively.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eMobility Scenario not cov-\u003c/p\u003e\u003cp\u003eered.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePaging optimization is pro-\u003c/p\u003e\u003cp\u003eposed, as Predictive-PEI (PPEI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGroup of end user made, based on their\u003c/p\u003e\u003cp\u003emobility patterns. Then predicts the current gNB of user and the expected new gNB basis KNN model.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86% efficient\u003c/p\u003e\u003cp\u003eprobabilistic method.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eusing the\u003c/p\u003e\u003cp\u003epaging\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eIts a MATLAB simulation,\u003c/p\u003e\u003cp\u003ewhere energy conversation is not the target, it reduces signaling overhead of the NW.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIn the mmW communica-\u003c/p\u003e\u003cp\u003etion, the paging informa- tion cannot be broadcasted simultaneously over all the beams.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUE indicates its presence in the gNB\u003c/p\u003e\u003cp\u003eidentified TX beam by transmitting BPI (Beam Presence Indicator) before PO.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eAuthors has performed the\u003c/p\u003e\u003cp\u003eMATLAB simulation for the proposed work\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMobility\u003c/p\u003e\u003cp\u003eexplored.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003easpect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003enot\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThis\u003c/p\u003e\u003cp\u003eWork\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEvaluate Battery Perfor-\u003c/p\u003e\u003cp\u003emance for VoNR and 5G VoLTE calls during Mobil- ity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExperiment conducted in live 5G SA\u003c/p\u003e\u003cp\u003enetwork across major chipsets.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eA detailed analysis of\u003c/p\u003e\u003cp\u003ebattery power consump- tion during VoNR and 5G VoLTE call during mobility in real network environment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eDo a deep analysis of\u003c/p\u003e\u003cp\u003ethe data and the statis- tical approaches for bet- ter understanding of MNO and end user scenarios\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eCross-slot scheduling in 5G New Radio (NR) is a technique to enhance resource allocation efficiency by allowing scheduling of user equipment (UE) resources across different time slots within a frame, rather than confining scheduling to a single slot. This approach enables more flexible and dynamic resource allocation. Using this, the NW performance is improved and latency is reduced. Cross-slot scheduling can better accommodate varying traffic patterns and optimize the use of available bandwidth. In the paper [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], the authors examines cross-slot scheduling effect during Voice over New Radio (VoNR) calls and high data throughput scenarios. The study was conducted using measurements on mobile devices using scenarios created by Rohde \u0026amp; Schwarz CMX500 5G test system. In this study, the researchers have introduced different delays between the Physical Downlink Control Channel (PDCCH) and the Physical Downlink Shared Channel (PDSCH). The results in this study displays reductions in battery consumptions, however the same approach is valid upto specific points. Beyond a specific points, the same approach shows diminishing results. Real world testing across various NW conditions and device modes would provide more realistic insights for this features\u0026rsquo; benefits and limitations.\u003c/p\u003e\u003cp\u003eIn another work [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] related to power consumption of voice call in SA and well as LTE NW, the test bed is used to simulate virtualized 4G core and 5G core NW as well as IMS NW. Power monitor equipment is used to measure the external power of the smartphone. Overall, this study concludes that during the experiment, the DUT consumes 38.88% less energy while making voice calls using 5G SA as compared to 4G. Existing VoNR selection procedures, defined by 3GPP, rely mainly on signal power levels and priority criteria from the network\u0026rsquo;s system information. The paper [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] proposes a novel protocol and algorithm that allows the UE to maintain a history of the availability of features like Semi-Persistent Scheduling (SPS), Connected Mode Discontinuous Reception (CDRX), and VoNR support in the serving cell\u0026rsquo;s area. This historical information is then used during systemn reselection evaluations, particularly when the UE is mobile. This work is a proposed theory and actual simulation or realisation is yet to be done.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn 5G Standalone (SA) network, \u0026rdquo;N1 mode supported\u0026rdquo; indicates that the network is capable of operating in a configuration where the 5G NR (New Radio) interface is used for both control signalling and user data, without relying on the LTE network. This means that the 5G SA network can fully manage all aspects of communication, including mobility. session management, and data transmission, entirely within the 5G infrastructure. 3GPP TS 24.501, Section 4.3.2 (Domain selection for UE originating sessions / calls) mentions about when UE shall disable the N1 mode capability for 3GPP access. VoNR call is not a good option for voice services when NR serving cell or the neighbouring NR cells are below a certain threshold. This work [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] relates to network procedures performed by a UE for managing a Voice over New Radio (VoNR) call. The method includes disabling Radio Resource Control (RRC) \u0026rdquo;Voice Over NR\u0026rdquo; capability bit to FALSE upon determining the signal strength of the 5G NR serving cell is less than a threshold value. Hence, under such condition, an Evolved Packet System (EPS) fallback process to establish the VoNR call over the 4G cell as a VoLTE call.\u003c/p\u003e\u003cp\u003eThe call setup time is also an important criteria in the cellular communication for the end user experience and the user equipment battery optimizations. In the work [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], under the controlled lab environment the findings confirms that VoNR is superior when compared to EPS Fall Back (EPSFB) in the faster call setup time. Various 3GPP releases were compared for the same in this work, although Rel-16 devices shows better call setup time when compared to previous releases.\u003c/p\u003e\u003cp\u003eGiven that the handover process is a critical operation during smartphone mobil- ity, and the continuity of Voice over New Radio (VoNR) calls is heavily dependent on its efficiency, a detailed literature review also conducted to investigate various optimization strategies for handovers in mobility scenarios, as discussed further.\u003c/p\u003e\u003cp\u003eThe seamless continuity of communication during handover (HO) procedures is a critical requirement for user experience in next-generation mobile networks. While significant research has been dedicated to HO optimization at the network level, the direct deployment of these solutions at the smartphone level presents considerable challenges. We examine various approaches, including conditional handover (CHO) enhancement, phase-specific delay analysis, context-aware strategies for vehicular net- works, and machine learning (ML)-based predictive methods. The discussion highlights the tension between network-centric control and UE-based autonomy, the computa- tional complexities associated with advanced algorithms, and the prevalent reliance on simulation-based validation, which often leaves the practical feasibility for smart- phone deployment an open question. Our review related to handover optimizations aims to provide a clear understanding of the current landscape, identifying promising research directions and the existing gaps in achieving efficient, UE-deployable HO solu- tions. Much of the existing literature focuses on HO optimization from the network\u0026rsquo;s perspective, where operators have prerogative control over HO decisions. However, implementing such controls directly on smartphones is often not feasible, as the HO logic is typically governed by the network infrastructure.\u003c/p\u003e\u003cp\u003eConditional Handover (CHO) represents an evolution from traditional HO mecha- nisms by introducing more detailed criteria for initiating a cell switch. Unlike normal HO, which is typically triggered by simple thresholds such as signal strength (e.g., when the serving cell\u0026rsquo;s signal quality degrades below a certain level and a neighbor- ing cell offers a stronger signal), CHO incorporates a broader set of conditions. These can include network congestion levels, specific user service requirements, or other per- formance metrics, aiming to make HO decisions more intelligent and context-aware. A study presented in [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] contributes to this area by proposing an optimization for the CHO process. The UE is configured with CHO parameters for \u0026lsquo;n\u0026lsquo; potential target cells, along with their respective feature values. The UE then continuously monitors all these CHO candidates. From this pool, it selects a subset of \u0026lsquo;m\u0026lsquo; candidates (denoted as C1, C2,. . ., Cm) that demonstrate better measurement results than the current serving cell. A Q-learning algorithm is subsequently applied to this refined list of can- didates to make an informed and optimized HO decision. This approach aims to make the CHO process more adaptive and intelligent by leveraging reinforcement learning to evaluate and select the most suitable target cell under dynamic network conditions. The efficiency of the HO process is often evaluated by analyzing the delays asso- ciated with its distinct phases. The work in [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] provides a detailed breakdown of\u003c/p\u003e\u003cp\u003ethese phases, categorizing them into preparation, execution, and completion. A key finding of this analysis is the significant variation in HO preparation delay between Non-Standalone (NSA) and Standalone (SA) 5G network architectures. In SA net- works, where the next-generation NodeB (gNB) operates independently, HO decisions based on UE measurement reports are transferred directly from the serving gNB to the target gNB. This direct communication path contributes to a relatively streamlined preparation phase. In contrast, NSA networks, which rely on an Evolved Universal Ter- restrial Radio Access Network (E-UTRAN) New Radio-Dual Connectivity (EN-DC) architecture, involve a master node (typically an LTE eNodeB) that intermediates the communication between the serving and target gNBs. This indirect path introduces additional latency, making the HO preparation delay more pronounced in NSA con- figurations compared to SA networks. The study quantifies this, noting that the HO preparation delay constitutes nearly 40% of the total HO time across various loca- tions and population densities. Furthermore, the execution phase of the HO is also scrutinized. A major contributor to delay during this phase is the downlink chan- nel synchronization time (Tsync), which is the time required for the UE to achieve downlink synchronization with the target gNB. On average, Tsync also accounts for approximately 40% of the HO execution delay, highlighting it as a critical area for optimization to reduce overall service interruption.\u003c/p\u003e\u003cp\u003eThe unique challenges of maintaining connectivity for high-speed users, particu- larly Connected Autonomous Vehicles (CAVs), have spurred dedicated research. In [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], a novel method tailored for millimeter-wave (mmWave) environments is proposed to reduce HO frequency. The Vehicular Frequency Reuse (VFR) scheme is central to this work. It distinguishes between low-speed and high-speed users by employing a sim- ple scalar metric known as the Velocity-Threshold (VT). Based on this threshold, users are served with different sets of communication channels. The VT value is not static but is adaptively calculated using the K-means machine learning algorithm. This algo- rithm clusters reported vehicle velocities to infer underlying road conditions, allowing the VT to dynamically adjust to the current traffic environment. The methodology was validated through extensive computer simulations. The results highlight that the VFR scheme effectively reduces air interface traffic load and significantly decreases HO rates. This dual reduction contributes to smoother and more efficient operation for the UE, which is particularly crucial for latency-sensitive vehicular applications.\u003c/p\u003e\u003cp\u003eRadio Link Failure (RLF) is a primary cause of Handover Failure (HOF), lead- ing to extended service interruptions that severely impact the Quality of Experience (QoE) for users in 5G New Radio (NR) networks. To address this, the work in [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] pro- poses a proactive HO strategy designed to initiate the handover process before the UE encounters an RLF, thereby minimizing HOF occurrences. The solution leverages a novel Machine Learning (ML) and beam measurement-based advanced HO algorithm. The ML model is trained using a set of network parameters derived from the UE\u0026rsquo;s serving cell, including Reference Signal Received Power (RSRP), Block Error Rate (BLER), Timing Advance (TA), and the direction of the serving beam. By analyz- ing these parameters, the algorithm can predict potential RLF conditions and trigger the HO in advance. The study specifically employs the K-Nearest Neighbors (KNN)\u003c/p\u003e\u003cp\u003ealgorithm for this predictive task. The overarching goal is to ensure minimal interrup- tion time and a high handover success rate, which are fundamental to maintaining a superior QoE in 5G NR systems.\u003c/p\u003e\u003cp\u003eA significant body of research explores the use of various ML algorithms to predict user mobility patterns, with the aim of optimizing HO decisions based on these pre- dictions. During our literature review, we found that these mechanisms hold promise for enhancing HO efficiency, they often introduce high computational costs at the UE. This raises a critical trade-off: the potential benefits of optimized HO must be weighed against the increased energy consumption, which may negate any battery savings. Fur- thermore, a large portion of these works are validated through simulations, leaving the actual performance and feasibility of deployment on commercial smartphones an area requiring further investigation.\u003c/p\u003e\u003cp\u003eOne such approach, proposed in [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], introduces a hybrid model for user mobility prediction in heterogeneous networks, where users transition between different types of base stations (e.g., macro cells and small cells). The method combines Markov Chain models, which are effective for modeling state transitions, with Artificial Neural Networks (ANNs), which can capture complex, non-linear patterns in user movement. This hybrid approach aims to improve the accuracy of mobility predictions, thereby enhancing overall HO management in diverse network environments.\u003c/p\u003e\u003cp\u003eSimilarly, the work in [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] presents an intelligent HO decision-making framework specifically designed for Vehicle-to-Everything (V2X) networks in 5G environments. This framework utilizes machine learning optimizations to predict vehicle mobility and network conditions proactively. By doing so, it aims to improve HO efficiency, mini- mize latency, reduce dropped connections, and optimize resource allocation, ensuring seamless connectivity for critical V2X applications. The framework\u0026rsquo;s performance was validated through simulation studies, demonstrating its potential benefits.\u003c/p\u003e\u003cp\u003eIn a different vein, the research in [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] focuses on mitigating frequent handovers in dense small-cell networks, a common problem due to the smaller coverage areas of these cells. The proposed HO procedure is based on mobility prediction, where the sys- tem anticipates user movement patterns. By forecasting these trajectories, the method can reduce unnecessary handovers, thereby improving network efficiency and the user experience. The authors emphasize that this is not a network-side optimization sim- ulation but rather a UE-centric approach. The reported results indicate a significant reduction in HO frequency, showcasing the potential of mobility prediction techniques in managing the complexities of small-cell deployments.\u003c/p\u003e\u003cp\u003eFuture research should prioritize the development of computationally efficient algo- rithms, real-world testing and validation, and a closer examination of the network-UE control paradigm to enable more intelligent, user-centric handover management that aligns with the demands of future mobile applications.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"3 Key Concepts","content":"\u003cdiv\u003e\n \u003cp\u003e5G Standalone (SA) and 5G Non-Standalone (NSA) represent two distinct architec- tural approaches for deploying 5G services, each with unique implications for energy consumption. 5G SA is a pure 5G deployment where the User Equipment (UE) con- nects directly to a 5G Core (5GC) network via the 5G New Radio (NR) interface. Active call control and media anchoring occur entirely within the 5G domain. The call flow for VoNR is depicted in Fig-1. This architecture eliminates dependence on the legacy LTE Evolved Packet Core (EPC). In this mode, Voice over New Radio (VoNR) becomes the native voice solution, managed entirely within the 5G infras- tructure. It is anticipated that VoNR will offer lower jitter and faster call setup times due to the enhanced NR radio interface and the use of advanced codecs such as the Enhanced Voice Service (EVS). Furthermore energy consumption is expected to be lower than that of Voice over LTE (VoLTE), as SA allows for more efficient radio resource utilization and incorporates advanced power-saving mechanisms.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003cp\u003eThe IP Multimedia Subsystem (IMS) [25] is a standardized architecture developed by the 3rd-Generation Partnership Project (3GPP) for delivering multimedia services- for instance, voice, video, and messaging-over IP networks. Although initially designed for 3G networks, IMS has evolved to support 5G VoNR and VoLTE voice services, enabling interoperability session control, and Quality of Service (QoS) across various access networks, including LTE, Wi-Fi, and NR.\u003c/p\u003e\n \u003cp\u003eDuring the initial stages of VoNR commercial deployment, several factors are con- sidered. Given that users often perceive voice services as more critical than data services, and in light of ongoing 5G coverage challenges and network optimization efforts, VoNR is not enabled across the entire network simultaneously. Instead, it is deployed in phases, initially in areas where the network meets stringent VoNR qual- ity requirements. Regions with mature 5G optimization will see commercial VoNR deployment first. In other areas, the VoNR function may remain deactivated, and the\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003cp\u003eEvolved Packet System (EPS) Fallback method will continue to be used for voice calls. This phased approach results in the emergence of VoNR functional switch boundaries [27].\u003c/p\u003e\n \u003cp\u003eThe various radio frequency bands acquired by telecom operators in India to date are summarized in Fig-2. Each band offers unique-benefits, and it is incumbent upon the operator to maximize its use for specific applications. Generally, the n28 band provides superior penetration characteristics, making it suitable for enhanced indoor coverage, while the n78 band is advantageous for broader outdoor coverage. Accord- ing to some reports, the n78 band can interfere with aircraft altimeters, leading to restrictions on its deployment in airport areas, Consequently, operators must utilize other NR bands in such locations. Lower frequency bands (Frequency Range 1, FR1) typically offer better coverage and penetration through obstacles but provide limited bandwidth. Conversely, higher frequency bands (Frequency Range 2, FR2) deliver larger bandwidths, enabling higher data rates, but suffer from limited coverage and are more susceptible to signal attenuation.\u003c/p\u003e\n \u003cp\u003eMoreover, due to support for multiple antennas and more powerful radio frequency modules in 5G NR, 5G devices tend to generate more heat than their LTE predeces- sors. This introduces new thermal management challenges for wireless devices. 3GPP Release 16 has introduced suggestions to mitigate this issue, such as allowing devices to latch onto the 4G radio from the 5G radio when they reach a specific temperature threshold. In addition to these standards, Original Equipment Manufacturers (OEMs) have also implemented device-specific optimizations to address thermal management. Physical Cell Identify (PCI) also plays a major role in the VoNR call signaling. In the 5G Network, the PCI change is associated with the cell reselection or redirection. However, if the PCI change involves switch to different cell within same NW, it is interpreted as handover-like process, especially during call continuity process. During RRC connected state, the transition in the serving cell identity for the user equipment signifies a handover (HO), which is essential for transferring an active communication session from one cell to another, ensuring seamless connectivity as user moves across NW. To simulate real-world conditions, a continuous data download was maintained in the background throughout our experiment.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Methodology","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Setup Configuration\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eOur experiment was conducted in a real world environment within the Noida area of Delhi NCR, India, as illustrated in Fig-3. The setup utilized both 4G and 5G Stan- dalone (SA) core network functions from a single Mobile Network Operator (MNO). The Devices Under Test (DUTs) were equipped with 5G SA SIM cards in the first SIM slot. Each DUT ran the Android 15 operating system, and all third-party appli- cations were uninstalled to prevent any background data activity from influencing the results. To monitor network parameters and count handovers, the G NetTack Lite\u0026rsquo; application [29] was installed on all DUTs. This dedicated software facilitates real- time monitoring of key metrics during drive testing, including time, serving Cell ID, RSRP, RSSI, speed, and Radio Access Technology (RAT) type. A screenshot of the application interface is provided in Fig-4.\u003c/p\u003e\n \u003cp\u003eEach Voice over New Radio (VoNR) mobile originated (MO) call was intended to last for 10 minutes. To evaluate battery performance during the drive tests, the battery level displayed on the DUT\u0026rsquo;s user interface was recorded at 10-minute intervals. If a\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eVoNR call experienced a fallback to LTE (Evolved Packet System Fallback, EPSFB), the call was terminated, and the system would wait for 5G SA network availability before placing the next VoNR call. The primary objective was to assess the impact of the VoNR and VoLTE calls in 5G SA network on DUT battery consumption. The frequency bands utilized during the experiment are detailed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The drive test vehicle maintained a speed between 60 and 80 kilometers per hour (KMPH), a typical speed for traffic in the test area.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLive 5G SA Network Configuration used in the experiment\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNR Bands (5G SA)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en78, n28\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLTE Bands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB3, B5, B40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNRCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en78 n28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003e4.2 Device Under Test (DUT)\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe device under test (DUTs) is an advanced mobile device as shown in Table-3. Specifications can also be referred at GSM Arena website [30].\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDUT Specifications\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpec\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDUT-1, DUT-2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDUT-3, DUT-4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDUT-5, DUT-6\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChipset\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQualcomm\u003c/p\u003e\n \u003cp\u003eSM8650-AC Snap-\u003c/p\u003e\n \u003cp\u003edragon 8 Gen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExynos 2400 (4 nm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMediatekRimen-\u003c/p\u003e\n \u003cp\u003esity, 6300 (6 nm)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel Name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSamsung S24 Ultra\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSamsung S24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSamsung A16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBattery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLi-Ion 5000 mAh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLi-Ion 4000 mAh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLi-Ion 5000 mAh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisplay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.8 inches\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.2 inches\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.7 inches\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResolution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1440 x 3120 pixels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1080 x 2340 pixels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1080 x 2340 pixels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGPU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdreno750(1\u003c/p\u003e\n \u003cp\u003eGH2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXcipse 940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMali-G57 MC2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOperating\u003c/p\u003e\n \u003cp\u003eSystem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eAndroid 15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Testing Methodology\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe battery consumption analysis followed a structured procedure. Initially, each DUT was fully charged to 100% and configured with a 5G SA SIM card in the first slot, while the second slot remained empty. All devices were running the Android 15 software. The drive test vehicle travelled at a speed of 60\u0026ndash;80 KMPH within the Noida NCR region, as per drive test location. We prepared one pair of devices for each of the three chipset types under examination: Qualcomm (QC), Exynos, and MediaTek, resulting in a total of six DUTs. For each chipset pair, VoNR was enabled on one device and disabled on the other, as depicted in Fig-5.\u003c/p\u003e\n \u003cp\u003eDuring mobility, a VoNR MO call was initiated on the first device of the pair, with the goal of maintaining a continuous connection for 10 minutes. Simultaneously, on the second device (where VoNR was disabled), a VoLTE call was triggered. Furthermore, a data download session from an FTP server was initiated on all DUTs, running in parallel with the active voice calls. This concurrent data activity was designed to keep the VoLTE device in a Radio Resource Control (RRC) connected state throughout the mobility test, ensuring a consistent and comparable testing scenario across all devices. To isolate the impact of voice services and data activity on battery consump- tion, other hardware components and software features- such as Bluetooth, GPS, accelerometers, Wi-Fi, NFC, and native Android applications (e.g., Gmail, Chrome)- were disabled. A total of two hours of drive testing was performed for each device pair. Throughout these tests, the mobile-terminated (MT) side of the call was main- tained at a stationary location approximately 10 kilometers away. This setup helped mitigate the risk of call drops attributable to failures at the MT end, such as Radio Link Failure (RLF), handover failure, or ping-pong effects. The overall experimental methodology was executed in several distinct stages, as outlined in Fig-6.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eIn total, the experiment was conducted for total 6 hours for both 5G VoNR and VoLTE technologies, encompassing 6 DUTs and collecting approx 90 data samples. The decision to conduct the experiment in a live, open-air environment was intentional and methodologically justified. This approach provides authentic network conditions, contrasting with the controlled, and often non- representative, settings of laboratory experiments or simulations. While laboratory environments can reduce measurement noise and simplify instrumentation, they frequently fail to capture the significant variations observed between theoretical measurements and real-world performance.\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5 Results","content":"\u003cdiv\u003e\n \u003cp\u003eThis paper presents an analysis of battery power consumption and handover fre- quency in 5G networks utilizing the IP Multimedia Subsystem (IMS), across various smartphone chipsets. A comparative evaluation is conducted between 5G Standalone (SA) and 4G LTE network architectures to demonstrate the corresponding impact on battery power consumption.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003cp\u003eFig-7, Fig-8, Fig-9 illustrates the total battery consumption during a drive test of 120 minutes approximately, encompassing three distinct chipsets: Qualcomm (QC), Exynos, and MediaTek (MTK). Throughout this drive test, the Device Under Test (DUT) transitioned between congested and decongested network environments, exe- cuting multiple handovers and Evolved Packet System Fallback (EPSFB) procedures. The findings indicate that in mobility scenarios, Voice over New Radio (VoNR) calls within a 5G SA network exhibit lower energy efficiency compared to Voice over LTE (VoLTE) calls. These results hold considerable significance for Original Equipment Manufacturers (OEMs) and Mobile Network Operators (MNOs) aiming to optimize smartphone energy efficiency and prolong battery life. VoLTE calls during the mobility scenario demonstrate the superior battery performance in contrast to the VoNR calls.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003cp\u003eThis disparity is power utilization shows the complexity of maintaining voice calls over 5G networks, which require more resources to ensure call quality and stability.\u003c/p\u003e\n \u003cp\u003eAs illustrated in Fig-7, the Qualcomm device exhibited 63% battery remaining after 110 minutes of Voice over New Radio (VoNR) calling. In comparison, a Voice over LTE (VoLTE) call of 118 minutes resulted in 66% remaining battery capacity. This data suggests that VoLTE technology demonstrates approximately 3% greater power efficiency under mobility conditions. It is important to note that practical con- straints during drive testing prevented the precise equalization of VoNR and VoLTE call durations. To ensure a robust analysis, the VoLTE call duration was intention- ally extended slightly beyond that of the VoNR call. A similar trend was observed in devices equipped with Exynos chipsets. Following 96 minutes of VoNR usage, the battery percentage was 57%, whereas a 110-minute VoLTE call maintained 60% bat- tery charge. This again points to a marginal efficiency advantage of approximately 3% for VoLTE over VoNR. Conversely, MediaTek devices displayed a more pronounced difference in battery consumption between the two calling technologies. For identical 118-minute call durations, VoNR and VoLTE usage resulted in 59% and 72% remain- ing battery power, respectively. This represents a significantly larger efficiency gap for VoLTE compared to VoNR than was observed in the Qualcomm and Exynos device tests.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003cp\u003eSimilar to the VoNR and VoLTE call in the 5G SA NW, handover events were also evaluated during our experiment under the same scenario of the drive testing for each chipset. The test result shows that HO counts contributes no significant impact of the smartphone battery performance across chipsets. However, at the same time, we observed that the HO count is more in VoLTE calls in comparison to VoNR calls. This observation is noteworthy, as lower HO is anticipated during the VoNR calls, as per the theoretical research as well. This is directly attributed to the practical implications of the NW planning, optimization techniques and resource allocations.\u003c/p\u003e\n \u003cp\u003eAn examination of Fig-10 reveals that Voice over New Radio (VoNR) calls exhibited the lowest HandOver (HO) count in Exynos devices, while the highest HO count was observed in MediaTek devices. Conversely, during Voice over LTE (VoLTE) calls, the HO count was highest in Qualcomm devices and lowest in those equipped with Exynos chipsets. Fundamentally, the data indicates that Exynos devices consistently demonstrate the lowest HO counts for both VoNR and VoLTE technologies. This trend suggests the presence of device-specific optimizations, potentially implemented by the Original Equipment Manufacturer (OEM), to enhance performance during mobility scenarios.\u003c/p\u003e\n \u003cp\u003eIn our experiment, wherein we evaluated the smartphone battery performance dur- ing 5G and 4G voice calls in mobility, the call sustainability is also on important aspect. During our testing we observed that VoNR call failed to maintain the connectivity for the full 10 minutes test duration in all the evaluated chipsets. During the testing, the EPSFB was frequently triggered to maintain the call continuity. This fallback to VoLTE preserved the active calls. Moreover, we observed that VoLTE calls has shown seamless continuity and this exhibit superior robustness in the dynamic environment. The analysis of battery depletion characteristics during mobility revealed a con- sistent trend across all tested chipsets, as illustrated in Fig-11. Irrespective of the chipset manufacturer- namely Qualcomm (QC), Samsung LSI (SLSI), or MediaTek (MTK)–the rate of battery depletion followed a remarkably similar pattern. This uni- formity suggests that the underlying technological framework and prevailing network conditions exert a more significant influence on battery performance than the specific chipset employed during VoNR and VoLTE call.\u003c/p\u003e\n \u003cp\u003eHandover efficiency represents another domain where distinct behavioral dif- ferences between VoNR and VoLTE were observed. Drive test results indicated a significantly lower number of handovers (HO) when the VoNR feature was enabled on the device, compared to when it was disabled. This reduction in HO frequency suggests that VoNR mode may employ enhanced network resource management strate- gies, potentially leading to fewer inter-cell transitions. However, this optimization in handover efficiency is accompanied by increased power consumption, a phenomenon noted in the preceding analysis.\u003c/p\u003e\n \u003cp\u003eFurthermore, the impact of varying radio conditions on battery performance was investigated across diverse environments, including expressways, commercial market areas, and congested urban zones. The drive test, covering a distance of approximately 20 kilometers, encompassed these varied radio propagation environments. Despite the heterogeneity in radio conditions, no significant impact on battery performance was observed. The consistency in battery depletion trends across these environments rein- forces the conclusion that network conditions are not a primary determinant of battery efficiency for VoNR and VoLTE calls during mobility.\u003c/p\u003e\n \u003cp\u003eIn conclusion, this comparative analysis of drive test data from devices equipped with QC, SLSI, and MTK chipsets highlights key performance differentials between VoNR and VoLTE technologies The study underscores significant differences in battery consumption, call sustainability, handover efficiency, and resilience to fluctuating radio conditions. While VoNR presents potential advantages in network resource optimiza- tion, its higher power consumption and current limitations in sustaining calls during mobility represent substantial challenges. Conversely VoLTE demonstrates greater sta- bility and power efficiency in dynamic scenarios, positioning it as the more reliable solution for voice communication in mobile contexts. As 5G infras, tructure continues to mature, further technological advancements in VoNR will be essential to reconcile the performance gap with VoLTE particularly concerning power efficiency and seamless handover management.\u003c/p\u003e\n \u003cp\u003eThese results also suggests that the performance of 5G SA VoNR call isn’t entirely independent of the activity on the 4G core and VoLTE network. MNOs can improve 5G SA performance by optimizing resource allocation strategies considering inter- connection with the 4G core network activity. The fact that VoNR activity influences 5G SA performance highlights the importance of considering cross-technology interac- tions when designing and operating multi-generation networks. VoNR calls has impact on the energy consumption and accounts considerable for 5G SA NW.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"6 Discussion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis section delves into the experimental results, offering contextual analysis and exploring their implications for future 5G network deployments and ongoing research. Multiple real-world factors within the 5G network (NW) ecosystem significantly influ- ence the battery performance of voice services. Parameters such as packet delays, retransmissions, signal quality, and frequent cell reselections or handovers directly affect the processing cycles and radio activity of the Device Under Test (DUT). In the current experiment, conditions were held constant for both Voice over New Radio (VoNR) and Voice over LTE (VoLTE) calls, ensuring parameter parity across both scenarios. However, these dynamic conditions are challenging to fully replicate in a laboratory environment, which typically benefits from stable radio frequency (RF) conditions and a static network topology. Consequently, field deployments may exhibit greater energy variability due to these fluctuating environmental factors, as evidenced by this work.\u003c/p\u003e\u003cp\u003eThe findings of this study are derived from observations in a live network, The 5G Standalone (SA) and 4G LTE networks have been operational since 2022 and 2016, respectively [31]. The VoNR service is still in the process of being deployed [32], and it is crucial to note that during this deployment phase on the SA network, smartphone power consumption for VoNR calls is higher compared to VoLTE services on the 4G network of the same Mobile Network Operator (MNO). While both technologies deliver high voice quality in realistic environments, VoLTE consistently demonstrates superior power efficiency acrosst various chipsets. The observed variance in 5G VoNR performance scores may indicate network instability, potentially leading to increased DUT battery consumption. This suggests that while the SA architecture offers initial advantages in speed and latency, VoLTE can still achieve better power conservation under comparable network conditions for voice calls.\u003c/p\u003e\u003cp\u003eThe overall results from our experiment shows that VoNR and VoLTE call in the mobility is influenced by a complex factors and not just the direct energy metrics. The other paraments which may affect the call continuity are NW radio conditions, radio resources\u0026rsquo;, allocation methodologies and codec selection. It\u0026rsquo;s important to note that the end user is not usually aware of these technical parameters. It\u0026rsquo;s also important to note that in this experiment various 3rd party app and background data activity were restricted, however they may have direct impact on the battery optimizations as well.\u003c/p\u003e\u003cp\u003eFuture Research Directions: Several promising avenues for future research emerge from this work, as follows:\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eCross-OEM Comparative Analysis: Undertake a comparative study of VoNR and VoLTE call performance across multiple Original Equipment Manufacturers (OEMs) to understand better about the OEM specific optimization can also affect battery performance.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSoftware Optimization: A test software build to check the impact of the velocity on the battery performance under varying speed during VoNR call. If the smartphone is prone to higher speed above threshold, the VoLTE can shall be preferred over VoNR to save the battery.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eDual SIM Impact: The impact of the dual SIM and the related performance in the live NW during mobility would be very interesting area to explore.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e"},{"header":"7 Conclusion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe widespread deployment of fifth-generation (5G) networks marks a significant milestone in mobile communications, enabling more efficient and dedicated voice packet transmission. This advancement is crucial for supporting real-time services, particularly Voice over New Radio (VoNR), which is the native voice solution for fully operational 5G Stand-Alone (SA) networks. By leveraging the full capabili- ties of 5G\u0026mdash;such as enhanced data rates, reduced latency, and improved spectral efficiency\u0026mdash;VoNR promises to deliver superior voice quality and overall network per- formance. Recent research has begun to investigate the comparative performance of VoNR and its 4G predecessor, Voice over LTE (VoLTE), with a particular focus on smartphone energy consumption. Notably, studies indicate that during mobile scenar- ios, VoNR can significantly reduce battery drain compared to VoLTE. Building on this foundation, our research aims to address a critical knowledge gap by conducting a comprehensive evaluation of both 5G VoLTE and VoNR services. We propose an experimental methodology designed to concurrently measure smartphone battery con- sumption within a live 5G SA network. By integrating objective technical parameters with an assessment of the estimated user experience, this study seeks to provide a more constructive understanding of the trade-offs between energy efficiency and voice qual- ity in next-generation mobile networks. Our observations revealed that battery drain was significantly higher during active VoNR calls in mobile scenarios compared to VoLTE calls. This finding contradicts results from previous laboratory-based studies, which suggested different performance characteristics.\u003c/p\u003e\u003cp\u003eThis study highlights the critical importance of device-side optimization strate- gies in addressing the battery consumption challenges posed by 5G NR technology. By focusing on practical, implementable solutions at the smartphone level, our research contributes to the development of more sustainable and efficient 5G mobile communication systems. The proposed VoNR-to-VoLTE switch mechanism dur- ing high-mobility scenarios offers a promising approach to balancing the advanced capabilities of 5G technology with the practical constraints of smartphone battery limitations.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDeclaration of Contribution:Manu Srivastava and Dr. Vimlesh Kumar Ray conceived of the presented idea. Manu Srivastava developed the theory and performed the experiment. Dr. Vimlesh Kumar Ray verified the methods, encouraged Manu Srivastava to investigate overall aspect and supervised this work and was in charge of overall direction and planning. Both the authors discussed the findings and contributed to the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDe Souza, C.B.B., Abularach Arnez, J.J., Fernandes, T., Tavares Alves, C.A., Sousa, J.O.: Analysis of power consumption in 4g volte and 5g vonr over ims net- work. In: 2022 IEEE 27th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), pp. 59\u0026ndash;64 (2022). https://doi.org/10.1109/CAMAD55695.2022.9966918\u003c/li\u003e\n\u003cli\u003eHe, P., Zhang, N., Liang, W.: Evaluation of the voice quality of vonr and volte. In: Proceedings of the 2024 6th International Symposium on Signal Processing Systems. SSPS \u0026rsquo;24, pp. 17\u0026ndash;21. Association for Computing Machin- ery, New York, NY, USA (2024). https://doi.org/10.1145/3665053.3665062 . https://doi.org/10.1145/3665053.3665062\u003c/li\u003e\n\u003cli\u003eWesley B. Conde, Y.h.S.B.J.J.A.A.C.B.B.d.S.B.R.X.R.F. 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Technol. \u003cstrong\u003e29\u003c/strong\u003e(2), 3270 (2018) https://doi.org/10.1002/ett.3270\u003c/li\u003e\n\u003cli\u003eArnez, J.A., Silva, W.A., De Souza, C.B., Sousa, J.O., Damasceno, M., Reis, R.G.: Real-time testbed for evaluating the battery consumption in 5g mobile networks and 5g voice calls over ip multimedia subsystem (ims). In: 2023 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications (APWC), pp. 104\u0026ndash;104 (2023). https://doi.org/10.1109/APWC57320.2023.10297518\u003c/li\u003e\n\u003cli\u003eConde, W.B., De Sousa, B.P.T., Barbosa, Y.H.S.: Evaluation of battery con- sumption and jitter between vinr and vonr over ims 5g network, 292\u0026ndash;294 (2023) https://doi.org/10.1109/IMOC57131.2023.10379787\u003c/li\u003e\n\u003cli\u003eOhashi, A., Pinheiro, B., Limma, P., Lopes, L., Barbosa, Y.H.S.: Analysis of battery consumption over vonr service and high data transfer on mobile devices using cross-slot scheduling feature. In: 2024 IEEE Latin-American Conference on Communications (LATINCOM), pp. 1\u0026ndash;5 (2024). https://doi.org/10.1109/LATINCOM62985.2024.10770660\u003c/li\u003e\n\u003cli\u003eKapoor, K., Basavaraj, D.P., Ramamoorthy, S., Vrind, T.: Novel method for increased power conservation in voice over nr (vonr) devices. EAI Endorsed Trans- actions on Cloud Systems \u003cstrong\u003e5\u003c/strong\u003e(15) (2019) https://doi.org/10.4108/eai.16-7-2019.162218\u003c/li\u003e\n\u003cli\u003eJangid Alok Kumar, K.R.P. Jha Kailash Kumar: Methods And Systems For Managing A Voice Over New Radio Call By A User Equipment. U.S. Patent 2021007635\u003c/li\u003e\n\u003cli\u003eDe Aguiar, M.F., Carvalho, J.K.S., Rodrigues, V.D.A., Chabi, A.F., Campos, J.V.D.S., Lopes, L.R., De Sousa, J.O.: Performance analysis of 5g voice solu- tions via eps fallback and vonr. In: 2023 IEEE Latin-American Conference on Communications (LATINCOM), pp. 1\u0026ndash;6 (2023). https://doi.org/10.1109/LATINCOM59467.2023.10361889 . IEEE\u003c/li\u003e\n\u003cli\u003eSundararaju, S.C., Ramamoorthy, S., Basavaraj, D.P., Phanindhar, V.: Advanced conditional handover in 5g and beyond using q-learning. In: 2024 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1\u0026ndash;6 (2024). https://doi.org/10.1109/WCNC57260.2024.10570840\u003c/li\u003e\n\u003cli\u003eGoh, Y., Oh, S., Kim, Y., Chung, J.-M.: Handover delay analysis of standalone and non-standalone 5g mobile networks. IEEE Wireless Communications \u003cstrong\u003e32\u003c/strong\u003e(3), 204\u0026ndash;211 (2025) https://doi.org/10.1109/MWC.009.2400163\u003c/li\u003e\n\u003cli\u003eRaeisi, M., Sesay, A.B.: Handover reduction in 5g high-speed network using ml- assisted user-centric channel allocation. IEEE Access \u003cstrong\u003e11\u003c/strong\u003e, 84113\u0026ndash;84133 (2023) https://doi.org/10.1109/ACCESS.2023.3297982\u003c/li\u003e\n\u003cli\u003eMishra, V., Das, D., Singh, N.N.: Novel algorithm to reduce handover failure rate in 5g networks. In: 2020 IEEE 3rd 5G World Forum (5GWF), pp. 524\u0026ndash;529 (2020). https://doi.org/10.1109/5GWF49715.2020.9221410\u003c/li\u003e\n\u003cli\u003eBahra, N., Pierre, S.: A hybrid user mobility prediction approach for handover management in mobile networks. Telecom \u003cstrong\u003e2\u003c/strong\u003e(2), 199\u0026ndash;212 (2021) https://doi.org/10.3390/telecom2020013\u003c/li\u003e\n\u003cli\u003eAl Harthi, F.R.A., Touzene, A., Alzidi, N., Al Salti, F.: Intelligent handover decision-making for vehicle-to-everything (v2x) 5g networks. 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In: 2022 International Conference on Information Processing and Network Provisioning (ICIPNP), pp. 39\u0026ndash;42 (2022). https://doi.org/10.1109/ICIPNP57450.2022.00015\u003c/li\u003e\n\u003cli\u003eSrivastava, M.: 5g network\u0026ndash;deployment, status and roadmap in indian tele- com ecosystem. Advances in AI for Biomedical Instrumentation, Electronics and Computing, 461\u0026ndash;465 (2024)\u003c/li\u003e\n\u003cli\u003eG-NetTrack; Gyokov Solutions \u0026mdash; gyokovsolutions.com. https://gyokovsolutions.com/g-nettrack/. [Accessed 18-09-2025]\u003c/li\u003e\n\u003cli\u003eGSMArena.com - mobile phone reviews, news, specifications and more... \u0026mdash; gsmarena.com. https://www.gsmarena.com/. [Accessed 18-09-2025]\u003c/li\u003e\n\u003cli\u003eDepartment of Telecom, Govt of India. https://dot.gov.in/. [Accessed 18-09- 2025]\u003c/li\u003e\n\u003cli\u003eJio claims first VoNR offering in India \u0026mdash; rcrwireless.com. https://www.rcrwireless.com/20250909/5g/jio-vonr-india#:\u0026sim;:text=Jio%20activates%20pan%2DIndia%20VoNR,battery%20use%20for%205G%20subscribers. [Accessed 18-09-2025]\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"VoNR, VoLTE, Smartphone, Mobility, 5G Standalone, Battery Consumption","lastPublishedDoi":"10.21203/rs.3.rs-7754087/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7754087/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe advent of 5G New Radio (NR) technology has introduced unprecedented capabilities in wireless communications, enabling enhanced throughput and sig- nificantly reduced latency. However, the substantial capabilities of 5G mobile communication have concurrently imposed considerable demands on the battery capacity of user equipment, particularly smartphones. 5G NR-capable smart- phones must support multiple NR frequency bands, utilize wide spectrum ranges through multiple antenna configurations, and maintain native voice call func- tionality in 5G Standalone (SA) networks via Voice over New Radio (VoNR). VoNR calls in 5G SA networks represent a critical native solution that presents significant opportunities for battery conservation.\u003c/p\u003e\u003cp\u003eIn this work, we conducted comprehensive experiments within live 5G SA network environments, utilizing smartphones equipped with all major chipset platforms available in the Android ecosystem. The primary objective was to investigate the impact on battery performance during active VoNR and VoLTE calls under mobility conditions, specifically when devices maintained Radio Resource Control (RRC) connected states. Our observations revealed that battery drain is higher during active VoNR calls in mobile scenarios com- pared to VoLTE calls. Notably, this finding contradicts results from previous laboratory-based studies, which suggested different performance characteristics. Furthermore, our experiments demonstrated that handover events occurred more frequently during VoLTE calls, a phenomenon that aligns with theoretical expec- tations in mobile network environments. The findings from this study provide valuable insights for Mobile Network Operators (MNOs) and Original Equip- ment Manufacturers (OEMs) regarding battery performance optimization during\u003c/p\u003e\u003cp\u003eVoNR calls in mobile scenarios. This research contributes to the existing body of knowledge by offering practical insights that can inform future resource alloca- tion strategies while maintaining optimal user experience for 5G NR smartphone users.\u003c/p\u003e","manuscriptTitle":"Experimental Insight on the Impact of VoNR and VoLTE Calls on the Smartphone Battery Performance during Mobility in 5G SA Network","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-01 09:01:30","doi":"10.21203/rs.3.rs-7754087/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a06d5c1c-6b96-4b25-851a-a8dd7c0c3605","owner":[],"postedDate":"October 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-07T07:08:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-01 09:01:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7754087","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7754087","identity":"rs-7754087","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}