Performance Evaluation of SINR in 5G Urban Macro-Cells with Variable Parameters Under different path loss models

Performance Evaluation of SINR in 5G Urban Macro-Cells with Variable Parameters Under different path loss models | 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 Performance Evaluation of SINR in 5G Urban Macro-Cells with Variable Parameters Under different path loss models ARUN AGARWAL, Sohail Khan, Shradha Suman Mohapatra, Sunil Kumar Sahoo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6570203/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 This research assesses the performance of Signal-to-Interference-plus-Noise Ratio (SINR) within urban macro-cells of 5G, comparing two different path loss models—Free-Space Propagation Model (FSPM) and Close-In (CI) model—to evaluate their effects on signal quality and network dependability. Concentrating on densely populated urban areas in Bhubaneswar, India, the study utilizes a hexagonal cell setup with 19 sites to investigate how crucial factors such as antenna height, inter-site distance, and transmission power affect SINR distribution. The simulations demonstrate that the CI model, which accounts for practical propagation effects such as shadowing and multipath fading, offers more accurate SINR predictions than the idealized FSPM. The results suggest that varying antenna heights enhances SINR by diminishing interference. The RSS analysis reveals that the Free-Space model often predicts higher signal strength than actual, whereas the Close-In model yields more precise outcomes for given environments. These findings highlight the necessity of refined deployment strategies and realistic modelling to strike a balance between coverage and performance in urban 5G networks. 5G Cellular Network Deployment Coverage Interference Management Network planning Signal Strength Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 I. INTRODUCTION At present, 4G wireless communication is in use worldwide, yet it has its limitations. The advent of fifth-generation (5G) wireless networks has established new standards for data speeds, latency, reliability, and user capacity, especially in densely populated urban regions. Urban macro-cells, which form the core of 5G coverage, need to tackle a variety of propagation challenges posed by the intricate physical environment, such as tall buildings, diverse street layouts, and dynamic user movement. Among the crucial elements affecting network performance, the Signal-to-Interference-plus-Noise Ratio (SINR) is a significant factor in determining communication quality, impacting throughput, spectral efficiency, and overall system reliability. Evaluating SINR performance in urban settings necessitates the use of dependable path loss models that forecast how signals weaken over distance and through various obstacles. The choice of an appropriate path loss model significantly influences the precision of performance analysis. This study examines two different path loss models: the Free Space Propagation Model and the Close-In (CI) Reference Distance Model. The Free Space Model presumes an ideal environment without obstructions, offering a baseline for signal attenuation under line-of-sight (LOS) conditions. Conversely, the Close-In Model provides a more realistic framework by considering path loss relative to a reference distance, thus accounting for environmental factors like building density and signal obstruction. Various configurations are mentioned further in this paper, such as antenna height, ISD (Inter-Site Distance), transmitter power, and carrier frequency, along with some fixed parameters to observe changes in coverage area and SINR values. Additionally, this research paper focuses on the signal strength between the transmitter and receiver antennas. The different configurations for signal strength are further discussed below, providing insights into how various parameters affect the strength at the location. The location chosen for our analysis is Bhubaneswar, India. The insights gained from this research aim to aid network designers and engineers in selecting suitable propagation models for specific deployment scenarios. By understanding how SINR performance varies across different models and system parameters, more efficient and reliable 5G network infrastructures can be developed, ultimately leading to improved Quality of Service (QoS) for users in urban macro-cell environments. The simulation block diagram for the research paper is illustrated below, offering a comprehensive overview and summary of the entire simulation process. TABLE I Evolution of Mobile Network Generations with Enhanced Features and Capabilities Generation Dara rate Innovations used Major Attributes 1G (1970-1980s) 14.4 Kbps AMPS,NMT,TACS Basic mobility, no data, poor security 2G (1990–2000) 9.6/14.4 Kbps TDMA,CDMA Voice and Data services 3G (2004–2005) 3.1 Mbps CDMA2000 (1xRTT,EVDO) UMTS And EDGE Mobile broadband, multimedia support 4G (2010 onwords) 100–300 Mbps High-speed internet,HD streaming, VoIP All-IP network, low latency,enhanced multimedia 5G (2020s) 1 to 10 Gbps IoT,automation, ultra-fastmobile broadband Ultra-low latency, high reliability, massive connectivity II. EXPERIMENTAL SECTION In general, raising the BS antenna height expands the service area by enhancing line-of-sight and decreasing signal obstruction, particularly in urban settings.However, if improperly handled, it might potentially increase interference. The simulation of 5G urban macrocell SINR uses four configuration scenarios (Configs A through D). A number of important parameters differ between these arrangements. The (BS) antenna height is fixed at 25 meters for the other configurations and varies from 10 to 100 meters in Config A. Config D use a fixed inter-site distance of 200 meters, but Config A and C utilize a variable of 200 meters, while Config B uses a wider range of 100 to 500 meters. Configs A, B, and D have a total transmit power of 44 dBm, but Config C has a range of 30 to 47 dBm. With the exception of Config D, which has a far larger frequency range of 0.9 GHz to 28 GHz, the carrier frequency is fixed at 4 GHz for all configurations. Thetransmit power \(\:{\text{P}}_{\text{t}}\) is given as: $$\:{\text{P}}_{\text{t}}\left(\text{d}\text{B}\text{m}\right)=10{\text{l}\text{o}\text{g}}_{10}\left(\text{P}\text{o}\text{w}\text{e}\text{r}\left(\text{W}\right)\right)+30$$ Let Tx power = 25 Watt Then \(\:{\text{P}}_{\text{t}}\left(\text{d}\text{B}\text{m}\right)=10{\text{l}\text{o}\text{g}}_{10}\left(25\:\text{W}\text{a}\text{t}\text{t}\right)+30\) \(\:{\text{P}}_{\text{t}}\left(\text{d}\text{B}\text{m}\right)\) =44 dBm Now the received signal power \(\:{\text{P}}_{r}\) at a distance d is given as: $$\:{\:\:\:\:\:\:\:\:\:\:\:\:\text{P}}_{\text{r}}\left(\text{d}\text{B}\text{m}\right)={\text{P}}_{\text{t}}+{\text{G}}_{\text{t}}+{\text{G}}_{\text{r}}-\text{F}\text{S}\text{P}\text{L}$$ Where, \(\:\mathbf{F}\mathbf{S}\mathbf{P}\mathbf{L}\left(\mathbf{d}\mathbf{B}\right)=20{\mathbf{l}\mathbf{o}\mathbf{g}}_{10}\left(\mathbf{d}\right)+20{\mathbf{l}\mathbf{o}\mathbf{g}}_{10}\left(\mathbf{f}\right)-147.55\) Let, distance = 200m frequency = 4Ghz= \(\:4\times\:{10}^{9}\) Hz transmitter gain = 0 dBi(isotropic) receiver gain = 0 dBi(isotropic) transmit power = 44 dBm Then, $$\:\text{F}\text{S}\text{P}\text{L}\left(\text{d}\text{B}\right)=20{\text{l}\text{o}\text{g}}_{10}\left(200\right)+20{\text{l}\text{o}\text{g}}_{10}\left(4\times\:{10}^{9}\right)-147.55$$ FSPL (dB) = 90.51 dB Now \(\:{\text{P}}_{\text{r}}\left(\text{d}\text{B}\text{m}\right)=44+0+0-90.51=\:-46.51\:\text{d}\text{B}\text{m}\) III. METHODOLOGY-PROPOSED PATH LOSS MODEL A. Free-Space Model A basic model for wireless communication, the Free-Space Propagation Model (FSPM) describes how electromagnetic signals move across a perfect environment devoid of obstructions, reflection, scattering, and absorption. It provides an upper bound on performance under optimal circumstances and is used as a baseline model in simulation-based investigations of wireless systems, such as 4G and 5G networks. Understanding the optimal signal behaviour and separating the effects of system characteristics like frequency, antenna height, and transmit power are the main objectives of academic research and early-stage system evaluations, where this model is most helpful.The FSPL is the reduction in power density of a signal as it propagates through free space. The log-distance formula for FSPL in decibels (dB) is: $$\:\mathbf{F}\mathbf{S}\mathbf{P}\mathbf{L}\left(\mathbf{d}\mathbf{B}\right)=20{\mathbf{l}\mathbf{o}\mathbf{g}}_{10}\left(\mathbf{d}\right)+20{\mathbf{l}\mathbf{o}\mathbf{g}}_{10}\left(\mathbf{f}\right)-147.55$$ 1 Alternatively, in terms of wavelength \(\:{\lambda\:}\) , the Friis Transmission Equation becomes: $$\:\:\:\:\mathbf{F}\mathbf{S}\mathbf{P}\mathbf{L}\left(\mathbf{d}\mathbf{B}\right)=20{\mathbf{l}\mathbf{o}\mathbf{g}}_{10}\left(\frac{4\varvec{\pi\:}\mathbf{d}}{\varvec{\lambda\:}}\right)$$ 2 where \(\:{\lambda\:}\) denotes wavelength of transmitted light in meters, f is the frequency in hertz, and d is the transmitter-to-receiver distance in meters.This demonstrates that FSPL rises with frequency and distance, indicating that attenuation is greater at higher frequencies (e.g., 4 GHz) than at lower ones within the same range [2].The received signal power \(\:{\text{P}}_{\text{r}}\) at a distance d is given as: $$\:{\:\:\:\:\:\:\:\:\:\:\:\:\text{P}}_{\text{r}}\left(\text{d}\text{B}\text{m}\right)={\text{P}}_{\text{t}}+{\text{G}}_{\text{t}}+{\text{G}}_{\text{r}}-\text{F}\text{S}\text{P}\text{L}$$ 3 In this case, \(\:{\text{P}}_{\text{t}}\) stands for transmit power in dBm, \(\:{\text{G}}_{\text{t}}\) for transmitter antenna gain (dBi), and \(\:{\text{G}}_{\text{r}}\) for receiver antenna gain (dBi).For our simulation parameters the antenna gains are assumed to be zero (isotropic antennas) so: $$\:{\:\text{P}}_{\text{r}}\left(\text{d}\text{B}\text{m}\right)={\text{P}}_{\text{t}}-\text{F}\text{S}\text{P}\text{L}$$ 4 An essential metric in wireless communication systems for assessing a network’s performance and quality is the Signal-to-Interference-plus-Noise Ratio (SINR). Interference is frequently overlooked while estimating the highest attainable SINR in the free-space model. The noise power is assumed to be main issue in this simplification. Thus, the SINR is defined as: $$\:\text{S}\text{I}\text{N}\text{R}=\frac{{\text{P}}_{\text{s}}}{{\text{P}}_{\text{i}}+{\text{P}}_{\text{n}}}$$ 5 Here, the SINR is a logarithmic indicator of signal quality and is given in decibels (dB). Where \(\:{\text{P}}_{\text{s}}\) the power of the desired signal is, \(\:{\text{P}}_{\text{i}}\) is power of the interference of the signal and \(\:{\text{P}}_{\text{n}}\) is the power of background noise. Understanding the upper bounds of network performance under optimal circumstances is made easier by this approximation, which focuses on the difference between the received signal power and the noise power. But in practice, environmental influences and other cell interference can greatly affect SINR, therefore sophisticated methods like noise reduction and interference management are needed to maximize network efficiency. B. Close-In Model One important and reliable model for analyzing large-scale signal loss in wireless channels is the Close-In (CI) Free Space Reference Distance Path Loss Model.It is especially proficient in showcasing signal performance throughout a varied frequency range, from sub-6 GHz to millimeter-wave (mmWave) bands, and has been recognized by 3GPPand IEEE for its effectiveness in modeling 5G scenarios in urban, suburban, and indoor environments. Introduced as a more straightforward option compared to the Alpha-Beta-Gamma (ABG) and floating intercept (FI) models, the CI model avoids the pitfalls of overfitting and ambiguous physical interpretation [12]. By linking the model to a specific physical reference—the free space path loss at 1 meter—it ensures both reliability and clarity in its application. The equation representing the CI model with a reference distance of \(\:{d}_{0}\) meters is articulated as: $$\:{\text{P}\text{L}}_{\text{C}\text{I}}\left(\text{f},\text{d}\right)=\text{F}\text{S}\text{P}\text{L}\left(\text{f},{\text{d}}_{0}\right)+10\text{n}{\text{l}\text{o}\text{g}}_{10}\left(\frac{\text{d}}{{\text{d}}_{0}}\right)+{\text{X}}_{{\sigma\:}}$$ 6 Where n is the Path loss exponent, characterizing the rate of signal decay and \(\:{X}_{\sigma\:}\) is the Log-normal shadow fading (in dB).In contrast to the FSPL model, the CI path loss model takes into greater consideration the real-life conditions in which signals are transmitted. While the FSPL model considers conditions using the Friis transmission equation, it is based on ideal conditions like clear line of sight with no obstructions. Obstacles, multipath fading, and environmental changes are challenges neglected by the FSPL. The CI model addresses shortcomings of the FSPL model relay focuses on the distance range greater than the reference distance, typically 1 meter in our case, while also providing flexibility based on the environment through path loss exponent. Unlike CI, FSPL does not consider urban, rural suburbs, or indoors. Alongside other innovations, CI has also built upon the foundation of shadow fading making it more effective at dealing with random signal fluctuations caused by environmental interruptions. CI however is quite effective when assuming NLOS conditions which are more frequent in everyday scenarios. In modern technology including 5G and mmWave communications which are more prone to high frequencies, CI has proven to work well as unlike other models, it maintains the dependence of frequency in its formulation[4–5]. IV. 5G NETWORK COVERAGE PLANNING Planning for 5G network coverage involves the strategic design and installation of base stations, antennas, and associated infrastructure to guarantee optimal wireless signal reach, capacity, and quality. Unlike earlier generations, 5G presents new challenges due to its use of higher frequency bands like mmWave, smaller cell sizes, and advanced technologies such as beamforming and massive MIMO. Successful coverage planning is crucial to fully realize the advantages of 5G, consisting of massive machine-type communication (mMTC), ultra-reliable low latency communication (URLLC), and enhanced mobile broadband (eMBB). The fundamentals of network coverage are covered in this section, along with specifics of the thorough 5G planning, such as transmitter site design and network topology. A. Understanding Network Coverage In the realm of wireless communication, network coverage refers to the specific geographical areas where a wireless network can connect its users. This coverage is shaped by the location and capabilities of network infrastructure, including cell towers and antennas. The quality of coverage is impacted by elements such as signal strength, frequency bands, and environmental factors. With the rise of 5G technology, achieving superior coverage is essential for enabling rapid data transfer, low latency, and uninterrupted connectivity across various environments. Wireless communication often employs a cell-based structure, where each cell corresponds to a particular geographic area served by a base station that facilitates communication with mobile devices within its range. This design allows for efficient frequency reuse, minimizes interference, and maximizes coverage. Cell sizes can vary, with smaller cells typically found in densely populated urban areas and larger cells in rural settings. This cell structure is integral to the management of network resources, ensuring smooth transitions between cells, and sustaining reliable connectivity as users move through different regions. In 5G networks, enhancements such as small cells and beamforming technology are utilized to further improve coverage and capacity. B. Defining Network Layout In contemporary 5G networks, particularly in densely populated urban regions with high user demand and interference, cell sites employ sectorization rather than omnidirectional antennas. Each site is segmented into three 120-degree sectors, enhancing performance through directional signal transmission, minimized interference, and frequency reuse. In a 19-site hexagonal configuration, this results in 57 sectors (19 × 3), ensuring effective coverage and smooth user transitions. Sectorization increases capacity by allowing each sector to utilize independent radio resources, accommodating more concurrent users [5]. Technologies such as massive MIMO and beamforming excel in this environment, enabling precise signal targeting for improved data rates and reduced interference. Furthermore, energy efficiency is improved, as sectors can dynamically adjust power based on traffic load, even shutting down during periods of low demand to conserve energy. Sectorization also streamlines network optimization, with adjustable parameters like antenna tilt and power per sector, ensuring robust performance in complex settings. In conclusion, a 57-sector, 19-site design is a strategic approach that optimizes coverage, capacity, efficiency, and spectral utilization, making it ideal for high-density 5G networks. C. Designing Transmitter Sites Three transmitters can be installed at each cell location. An array of antenna elements or a single antenna element can be used to create transmitters. Given the usage of higher frequency bands (millimetre-wave bands) in 5G, the incorporation of numerous antennas into one arrayis feasible, for example, an 8-by-8 rectangular array with an operating frequency of 30 GHz, according to IMT-2020 [6]. V. SIMULATION PARAMETERS TABLE II Simulation Parameters For 5G Network Coverage And Sinr Parameters Value Bandwidth 80 Mhz BS Antenna gain 10 dBi BS Antenna noise figure 7 dB Receiver height 1.5m Receiver noise figure 7dB Receiver gain 8 dBi No. of cell sites 19 VI. RESULTS, DISCUSSION AND PERFORMANCE ANALYSIS To analyse the SINR (Signal-To-Interference-Noise-Ratio) iterations are made on antenna height, Inter-site distance and transmitter power by keeping the different parameters fixed for each case mentioned Table III. Recognizing that actual coverage can vary greatly depending on deployment location, this paper estimates 5G network coverage using the close in model and the free space propagation model. We have supplied the geographical latitude and longitude coordinates of the rectangle antenna that we use in our design. Bhubaneswar has been chosen as the venue. The transmitter is located at latitude 20.296059 N and longitude 85.824539 E. The different cases for the SINR simulations are depicted in Figs. 5 – 10 . The simulation complies with the IMT 2020 standards for planning and simulating coverage in 5G networks. A comprehensive list of all simulation parameters for different configurations can be found in Table III. TABLE III Parameter Configurations for 5G Urban Macrocell SINR Simulation Scenarios Parameters ConfigA Config B Config C Config D BS antenna height 10m-100m 25m 25m 25m Inter-site distance 200m 100m-500m 200m 200m Total transmit power 44dBm 44dBm 30dBm-47dBm 44dBm Carrier frequency 4Ghz 4Ghz 4Ghz 0.9Ghz-28Ghz A. Results For Free-Space Model Figure 5 illustrates the SINR distribution within a 5G urban macrocell setting for antenna heights of 10 meters (left) and 100 meters (right), maintaining a consistent inter-site distance of 200 meters. At a height of 10 meters, the signal coverage is more concentrated, leading to higher SINR and reduced interference due to limited propagation and minimal overlap with adjacent cells [10]. However, this can result in coverage gaps in densely populated urban areas. Conversely, the 100-meter antenna height offers broader coverage but also increases inter-cell interference, which reduces the overall SINR. This comparison underscores the balance between signal quality and coverage when selecting antenna heights. Figure 6 depicts a comparison of SINR distributions for two distinct inter-site distances (ISD) within a 5G urban macrocell setting, maintaining a constant base station antenna height of 25 meters. The left map depicts an ISD of 500 meters, while the right map illustrates an ISD of 100 meters. In the scenario with a 500 m ISD (left), coverage is less comprehensive, and SINR performance is diminished, particularly in outer regions such as Jayadev Vihar, due to the greater distance between base stations. As a result, signal strength is weaker, leading to potential performance issues for users located at the periphery. Conversely, the 100 m ISD scenario (right) reveals a marked enhancement in SINR across the area, including Jayadev Vihar, attributed to the closer placement of base stations. Figure 7 illustrate the effects of different transmit power levels in a 5G urban macrocell setting. The left image corresponds to a transmit power of 30 dBm, while the right image reflects a power level of 47 dBm, with all other parameters remaining unchanged. It is apparent that a higher transmit power significantly improves both signal quality and coverage area, as evidenced by the more extensive high SINR regions in the right image. In this scenario increasing the transmitter power might also increase interference to neighbouring cells, but this is often outweighed by the strong signal. The design of the SINR map, characterized by hexagonal patterns from the cell layout, will remain unchanged, but the colour gradients that reflect SINR values will be adjusted. B. Result For Close-In Model Figure 8 illustrate the SINR distribution according to the Close-In model for antenna heights of 10 meters (left) and 100 meters (right). At 10 meters, the SINR is higher because of less interference and more concentrated coverage. However, at 100 meters, the wider coverage results in greater inter-cell interference, leading to a lower overall SINR across the region. Thus, by comparing the free space model and close in model we can clearly depicts that the coverage gaps are reduced in the SINR map and the SINR values are reduced due to large interference. Figure 9 presents a comparison of SINR distributions for two different inter-site distances (ISD) in a close-in model, with the base station antenna consistently set at a height of 25 meters. The map on the left shows an ISD of 100 meters, whereas the map on the right displays an ISD of 500 meters. These maps suggest a similar analysis, indicating that coverage gaps are minimized compared to the free space model. Figure 10 is expected to undergo a similar analysis. Therefore, from the comparison above, we can confirm that the close-in model enhances coverage gaps, although it does so by diminishing signal strength and other parameters. C. Result of Signal Strength for Free-space and Close-In Models Table IV illustrate, the received signal strength (RSS) in dBm is displayed for various transmit power levels, contrasting the Free-Space and Close-In propagation models in Bhubaneswar. As the transmit power rises from 1W to 50W, both models demonstrate an increase in RSS, signifying stronger signals. The Free-Space model consistently provides higher RSS values compared to the Close-In model, which is due to its idealized assumptions that minimize environmental losses [11]. On the other hand, the Close-In model takes into account real-world urban barriers, resulting in greater path loss and diminished signal strength. For example, at a transmit power of 1W, the Free-Space model shows an RSS of -74.39 dBm, whereas the Close-In model reflects a lower − 90.76 dBm. Even at 50W, the Close-In model's RSS of -73.46 dBm is still less than the Free-Space model's -57.41 dBm. This indicates that the Free-Space model may overestimate signal strength, while the Close-In model provides a more realistic view of signal degradation in urban environments such as Bhubaneswar. TABLE IIIV Received Signal Strength (in dBm) vs Tx Power using Free Space and Close-In Models in Bhubaneswar Power (Watt) Free-Space (dBm) Close-In(dBm) 1 -74.3973132323568 -90.7571545084454 5 -67.4076131889966 -83.5292681390751 10 -64.3973132323568 -80.5990250137152 20 -61.387013275717 -77.6838635506857 50 -57.4076131889966 -73.4620595914008 Table IV demonstrates that in the urban setting of Bhubaneswar, increasing the height of antennas enhances signal strength, though the extent of improvement varies across different models. The free space model forecasts stronger signals, reaching − 58 dBm at a height of 10 meters. In contrast, the more realistic close-in model indicates weaker reception at -74 dBm at the same height, with smaller enhancements due to urban obstructions. The disparity between the models underscores the impact of Bhubaneswar's buildings and vegetation on signal degradation, emphasizing the importance of raising tower heights to 10–100 meters to counteract real-world path loss. TABLE V Received Signal Strength (in dBm) vs Antenna Height using Free Space and Close-In Models in Bhubaneswar Antenna Height (m) Free-Space (dBm) Close-In(dBm) 10 -58.1914977032885 -74.1145716954686 25 -58.1924812209569 -74.1526033111467 40 -58.1943076595538 -74.5491439742817 55 -58.1969759560513 -74.4373383560491 100 -58.2100140600042 -74.9830746535056 Table V demonstratesthat higher frequencies exhibit a more drastic reduction in signal strength in real-world applications than theoretical models suggest for free space. The sub-6GHz bands (700MHz-3.5GHz) ensure better coverage, while mmWave frequencies (24GHz and above) encounter severe losses, limiting their feasibility for widespread deployment. In urban areas, signal strength decreases by 25-30dB per decade in close-in models, in contrast to a 20dB decrease in free space, with the performance gap increasing at higher frequencies. TABLE VI Received Signal Strength (in dBm) vs Carrier Frequency using Free Space and Close-In Models in Bhubaneswar Carrier Frequency (Ghz) Free-Space (dBm) Close-In(dBm) 0.9 -52.1723965050871 -61.1073046095007 1.8 -58.1929964183668 -75.6132366801209 3.5 -63.9689072033061 -97.5137072126748 26 -81.387013275717 -329.708468687703 28 -82.030706943145 -349.605752587733 VII. ANALYSIS OF CHALLENGES The Free Space Model is based on the premise of a direct line-of-sight (LOS) and the absence of obstacles, which makes it ideal for open spaces but problematic in urban environments. It often overestimates coverage since it overlooks the effects of reflection, diffraction, and scattering. In non-line-of-sight (NLOS) conditions, it predicts unrealistically high SINR values. Furthermore, it fails to consider shadowing and multipath phenomena that are significant in city landscapes, resulting in inaccuracies for scenarios involving street canyons, indoor users, or areas with obstructions. The Close-In model enhances realism by using a reference distance but has limitations: it's a single-parameter model that may oversimplify multi-slope urban path loss [14]. Accurate performance requires careful tuning of the path loss exponent (PLE) for specific environments (urban macro/micro, indoor). Additionally, it doesn't model fast fading and must be combined with stochastic models for complete channel characterization. Urban SINR modelling presents significant challenges due to factors like multipath fading from closely packed infrastructure, shadowing effects, frequent non-line-of-sight (NLOS) scenarios, fluctuating interference patterns, and channel variations driven by mobility. To achieve accurate predictions in cities like Bhubaneswar, it is crucial to have detailed 3D antenna characteristics and extensive geospatial information, which are often not readily available. Consequently, advanced modelling strategies that include real-world calibration are required [15]. VIII. CONCLUSIONS AND FUTURE WORK This research illustrates the impact of antenna height, inter-site distance (ISD), transmission power, and propagation models on SINR and signal strength in 5G urban macrocells. Antennas positioned at lower heights (10 m) yield higher SINR but can lead to coverage gaps, whereas taller antennas (100 m) extend coverage but introduce more interference. Shorter ISDs (100 m) help reduce coverage gaps, facilitating dense urban deployment. The RSS analysis indicates that the Free-Space model tends to overestimate signal strength, while the Close-In model provides more accurate results for environments such as Bhubaneswar. These results underscore the importance of optimized deployment strategies and realistic modelling to achieve a balance between coverage and performance in urban 5G networks. Abbreviations MIMO Multiple–input–Multiple–output SINR Signal to Interference Noise Ratio FSPL Free space path loss CI Close–in model LOS Line of sight NLOS Non–line of sight RSS Received Signal Strength QOS Quality of Service 5G 5th Generation ABG Alpha–Beta–Gamma MMTC Massive machine type communication URLLC Ultra–reliable low latency communication EMBB Enhanced mobile broadband Declarations Availability of data and material “Not Applicable” Competing interests “The authors declare that they have no competing interests" Funding “The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.” Authors' contributions "1st Author analyzed and done whole literature review. 2nd and 3rd was a major contributor in carrying out the work and writing the manuscript. All authors read and approved the final manuscript." 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A. Gupta and R. K. Jha, A survey of 5G network: architecture and emerging technologies, IEEE Access, vol. 3 (2015), pp. 1206-1 Report ITU-R M.2135-1, "Guidelines for evaluation of radio interface technologies for IMT-Advanced", 2009. https://www.itu.int/dms_pub/itu-r/opb/rep/R-REP-M.2135-1-2009-PDF-E.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6570203","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":455632795,"identity":"d657d870-b737-4dc1-a917-7a5bc01594d0","order_by":0,"name":"ARUN AGARWAL","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYHAD5sYHMOYBIrUwNhuQrKVNgih1/LPPmD348euevMHxg22VPyrsGPjbDzAeLsCjReJcjrlhb1+x4YYziW23ec4kM0icSWA4PAOfNWd4zCR4exIYtx0AamFsY2ZguMHAcJgHjw55oBbJvz0J9tvOP2wr/NlWzyBPSIsBUIs0z4+ExG03EtsYeNsOMxgQ0mJ4hq1MWrYhIXn/jYfN0jxnjvMYnklswKtF7gzzNsk3fxJsZ/YnH/z4o6JaTu744cOf8WkBA8Y2BBuomLGBkAYg+EOEmlEwCkbBKBi5AACgsU9uPFrt1QAAAABJRU5ErkJggg==","orcid":"","institution":"Siksha O Anusandhan University Institute of Technical Education and Research","correspondingAuthor":true,"prefix":"","firstName":"ARUN","middleName":"","lastName":"AGARWAL","suffix":""},{"id":455632796,"identity":"fab35ccc-bd49-4e20-aaef-d4d81e71ebaf","order_by":1,"name":"Sohail Khan","email":"","orcid":"","institution":"Siksha O Anusandhan","correspondingAuthor":false,"prefix":"","firstName":"Sohail","middleName":"","lastName":"Khan","suffix":""},{"id":455632797,"identity":"a9c839e9-10a6-4af6-9405-f6ef02b12495","order_by":2,"name":"Shradha Suman Mohapatra","email":"","orcid":"","institution":"Siksha O Anusandhan","correspondingAuthor":false,"prefix":"","firstName":"Shradha","middleName":"Suman","lastName":"Mohapatra","suffix":""},{"id":455632798,"identity":"07db1b72-f106-44ce-b7a5-197153e5d57e","order_by":3,"name":"Sunil Kumar Sahoo","email":"","orcid":"","institution":"Siksha O Anusandhan","correspondingAuthor":false,"prefix":"","firstName":"Sunil","middleName":"Kumar","lastName":"Sahoo","suffix":""}],"badges":[],"createdAt":"2025-05-01 08:03:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6570203/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6570203/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82777360,"identity":"4865bd10-6507-4107-83fc-0a536762b3b2","added_by":"auto","created_at":"2025-05-15 07:35:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":46494,"visible":true,"origin":"","legend":"\u003cp\u003eSimulation block diagram of the proposed communication system.\u003c/p\u003e\n\u003cp\u003eThe figure illustrates whole digital communication system, emphasizing the digital information transmission and reception process. The information source, which creates the message to be conveyed, comes first. This data is compressed by the source coder, and redundancy is added for error correction by the channel coder. After the modulator transforms the digital signal into a transmittable format, the channel is shared via multiple access mechanisms. After that, the signal travels along the propagation channel. Separating the intended user isolates the signal, whereas diversity combining increases signal dependability at the receiver. The demodulator recovers the baseband signal while the equalization reduces channel distortions. The source decoder reconstructs the original message for delivery to the information sink, whereas the channel decoder fixes mistakes. This framework guarantees accurate and effective digital communication.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6570203/v1/ecb576d77d1143fc67dfbecc.png"},{"id":82778476,"identity":"dff8e820-67d4-4cf2-a57c-ff2831de6483","added_by":"auto","created_at":"2025-05-15 07:43:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":38919,"visible":true,"origin":"","legend":"\u003cp\u003eFree-space model with line-of-sight path over distance d between transmitter and receiver.\u003c/p\u003e\n\u003cp\u003eA simple line-of-sight (LOS) communication situation that is frequently employed in wireless communication models is depicted in the figure. It displays a receiver (like a moving car) on the right at a height hm and a transmitter (like a base station or cell tower) on the left, raised at a height hb. The direct path of signal propagation is shown by the straight line connecting the transmitter and receiver. The symbol \"d\" represents the horizontal distance between the transmitter and receiver. Path loss models, such as the free-space propagation model or the two-ray ground reflection model, frequently use this configuration to examine how signals behave over long distances in open spaces.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6570203/v1/2c79c6e77aa157f943c1ab1c.png"},{"id":82777367,"identity":"37e99fbf-07fd-4618-bf28-714be60e3dac","added_by":"auto","created_at":"2025-05-15 07:35:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":56639,"visible":true,"origin":"","legend":"\u003cp\u003eClose-in model showing signal reflections and diffractions between transmitter and receiver\u003c/p\u003e\n\u003cp\u003eSignal transmission in urban settings with obstructions like buildings is shown by the close-in propagation model. Multipath propagation results from the reflection, diffraction, and scattering of signals. Compared to free-space models, this model more accurately estimates route loss by taking into consideration obstacles between the transmitter and receiver, particularly in urban and mobile settings.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6570203/v1/d93f84e07847036b40ced7d4.png"},{"id":82777362,"identity":"69a4461a-8ceb-4396-a2f5-a9e875c0c927","added_by":"auto","created_at":"2025-05-15 07:35:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":78780,"visible":true,"origin":"","legend":"\u003cp\u003eHexagonal Cellular Network Layout with Inter-Site Distance (ISD).\u003c/p\u003e\n\u003cp\u003eA multi-cell hexagonal cellular network structure, which is frequently used to simulate wireless communication situations, is depicted in this figure. The Inter-Site Distance (ISD) shows the distance between adjacent base stations, and each hexagon represents a cell that a base station serves.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6570203/v1/5c99bed5c33576718c787a77.png"},{"id":82778914,"identity":"a7e3e758-8f62-40e1-8e35-7ceba3e58307","added_by":"auto","created_at":"2025-05-15 07:51:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":117551,"visible":true,"origin":"","legend":"\u003cp\u003eConfiguration A-SINR Map Comparison for Different Antenna Heights (ISD=200m).\u003c/p\u003e\n\u003cp\u003eSINR distributions at antenna heights of 10 m (left) and 100 m (right) with a fixed inter-site distance of 200 m are shown in this picture. Taller antennas offer greater coverage but increase inter-cell interference, which lowers total SINR. In contrast, lower antennas produce better SINR and less interference.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6570203/v1/0f119a3a6f7bfbd025da1e8a.png"},{"id":82777370,"identity":"9272757d-eb97-4a56-b25f-4cc10d1a01e1","added_by":"auto","created_at":"2025-05-15 07:35:03","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":135906,"visible":true,"origin":"","legend":"\u003cp\u003eConfiguration B-SINR Map Comparison for Different Inter-site distance (BS Antenna height=25m).\u003c/p\u003e\n\u003cp\u003eSINR distributions for ISDs of 500 m and 100 m in a 5G urban macrocell configuration with a 25 m antenna height are contrasted in the figure. Due to denser base station deployment, the 100 m ISD offers noticeably better SINR than the 500 m ISD, which exhibits poorer coverage and SINR, particularly near edges.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6570203/v1/c63e81e6db8b474209bf58b2.png"},{"id":82778479,"identity":"0affb6b7-ea19-454e-97d4-356b900d6cb3","added_by":"auto","created_at":"2025-05-15 07:43:03","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":124739,"visible":true,"origin":"","legend":"\u003cp\u003eConfiguration C-SINR Map Comparison for Different transmitter power.\u003c/p\u003e\n\u003cp\u003eSINR distributions for transmit powers of 30 dBm (left) and 47 dBm (right), with constant parameters, are displayed in this figure. High SINR regions are increased by higher transmit power, which also improves signal quality and coverage. The stronger signal usually improves overall performance without changing the hexagonal layout pattern, even if interference may increase.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-6570203/v1/de5cea321540036afc1708c1.png"},{"id":82778919,"identity":"d7d26fe2-7ed0-4633-bb68-5aba838c6308","added_by":"auto","created_at":"2025-05-15 07:51:03","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":114499,"visible":true,"origin":"","legend":"\u003cp\u003eConfiguration A- SINR Map Comparison for Different antenna height (ISD=200m) for Close-In model.\u003c/p\u003e\n\u003cp\u003eBased on the Close-In path loss model, SINR distributions for antenna heights of 10 and 100 meters are shown in this image. Higher antenna height increases coverage but decreases SINR because of more inter-cell interference, whereas lower antenna height results in higher SINR because of less interference. The Close-In model has less coverage gaps than the Free Space model, but because of greater interference, the SINR levels are also lower.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-6570203/v1/00fac23941c61a31aa79a816.png"},{"id":82777371,"identity":"a44ae96d-7af3-43cf-8c31-49aa3e7f8068","added_by":"auto","created_at":"2025-05-15 07:35:03","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":119023,"visible":true,"origin":"","legend":"\u003cp\u003eConfiguration B- SINR Map Comparison for Different Inter-site distance (BS Antenna height=25m) for Close-In model.\u003c/p\u003e\n\u003cp\u003eWith a fixed antenna height of 25 m, this figure contrasts SINR distributions for inter-site distances of 100 m and 500 m under the Close-In model. While SINR effectiveness still varies with ISD, the results demonstrate fewer coverage gaps as compared to the Free Space model.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-6570203/v1/dafb57f32c5f6f28e3fd392d.png"},{"id":82778478,"identity":"8e8d4da9-b2c2-4031-b28d-c23c3ec23770","added_by":"auto","created_at":"2025-05-15 07:43:03","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":120235,"visible":true,"origin":"","legend":"\u003cp\u003eConfiguration C- SINR Map Comparison for Different transmitter power for Close-In model.\u003c/p\u003e\n\u003cp\u003eThis figure is anticipated to go through a comparable analysis. Thus, it is evident from the comparison above that the close-in model increases coverage gaps, albeit by reducing signal strength and other factors.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-6570203/v1/f5b4567b97dac4df3a02fba0.png"},{"id":84668606,"identity":"49b58abd-539d-4368-836b-b96dec6ce5d3","added_by":"auto","created_at":"2025-06-16 06:19:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1580837,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6570203/v1/7c8cea19-3cd9-4db3-81e9-0033c8f9d028.pdf"}],"financialInterests":"","formattedTitle":"Performance Evaluation of SINR in 5G Urban Macro-Cells with Variable Parameters Under different path loss models","fulltext":[{"header":"I.\tINTRODUCTION","content":"\u003cp\u003eAt present, 4G wireless communication is in use worldwide, yet it has its limitations. The advent of fifth-generation (5G) wireless networks has established new standards for data speeds, latency, reliability, and user capacity, especially in densely populated urban regions. Urban macro-cells, which form the core of 5G coverage, need to tackle a variety of propagation challenges posed by the intricate physical environment, such as tall buildings, diverse street layouts, and dynamic user movement. Among the crucial elements affecting network performance, the Signal-to-Interference-plus-Noise Ratio (SINR) is a significant factor in determining communication quality, impacting throughput, spectral efficiency, and overall system reliability. Evaluating SINR performance in urban settings necessitates the use of dependable path loss models that forecast how signals weaken over distance and through various obstacles. The choice of an appropriate path loss model significantly influences the precision of performance analysis. This study examines two different path loss models: the Free Space Propagation Model and the Close-In (CI) Reference Distance Model. The Free Space Model presumes an ideal environment without obstructions, offering a baseline for signal attenuation under line-of-sight (LOS) conditions. Conversely, the Close-In Model provides a more realistic framework by considering path loss relative to a reference distance, thus accounting for environmental factors like building density and signal obstruction. Various configurations are mentioned further in this paper, such as antenna height, ISD (Inter-Site Distance), transmitter power, and carrier frequency, along with some fixed parameters to observe changes in coverage area and SINR values. Additionally, this research paper focuses on the signal strength between the transmitter and receiver antennas. The different configurations for signal strength are further discussed below, providing insights into how various parameters affect the strength at the location. The location chosen for our analysis is Bhubaneswar, India. The insights gained from this research aim to aid network designers and engineers in selecting suitable propagation models for specific deployment scenarios. By understanding how SINR performance varies across different models and system parameters, more efficient and reliable 5G network infrastructures can be developed, ultimately leading to improved Quality of Service (QoS) for users in urban macro-cell environments. The simulation block diagram for the research paper is illustrated below, offering a comprehensive overview and summary of the entire simulation process.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eTABLE I Evolution of Mobile Network Generations with Enhanced Features and Capabilities\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDara rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInnovations used\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMajor Attributes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1G\u003c/p\u003e \u003cp\u003e(1970-1980s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.4 Kbps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAMPS,NMT,TACS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBasic mobility, no data, poor security\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2G\u003c/p\u003e \u003cp\u003e(1990\u0026ndash;2000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.6/14.4 Kbps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTDMA,CDMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVoice and Data services\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3G\u003c/p\u003e \u003cp\u003e(2004\u0026ndash;2005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.1 Mbps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCDMA2000\u003c/p\u003e \u003cp\u003e(1xRTT,EVDO)\u003c/p\u003e \u003cp\u003eUMTS And EDGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMobile broadband, multimedia support\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4G\u003c/p\u003e \u003cp\u003e(2010 onwords)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u0026ndash;300 Mbps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh-speed internet,HD streaming, VoIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAll-IP network, low latency,enhanced multimedia\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5G\u003c/p\u003e \u003cp\u003e(2020s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 to 10 Gbps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIoT,automation, ultra-fastmobile broadband\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUltra-low latency, high reliability, massive connectivity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"II.\tEXPERIMENTAL SECTION","content":"\u003cp\u003eIn general, raising the BS antenna height expands the service area by enhancing line-of-sight and decreasing signal obstruction, particularly in urban settings.However, if improperly handled, it might potentially increase interference.\u003c/p\u003e \u003cp\u003eThe simulation of 5G urban macrocell SINR uses four configuration scenarios (Configs A through D). A number of important parameters differ between these arrangements. The (BS) antenna height is fixed at 25 meters for the other configurations and varies from 10 to 100 meters in Config A. Config D use a fixed inter-site distance of 200 meters, but Config A and C utilize a variable of 200 meters, while Config B uses a wider range of 100 to 500 meters. Configs A, B, and D have a total transmit power of 44 dBm, but Config C has a range of 30 to 47 dBm. With the exception of Config D, which has a far larger frequency range of 0.9 GHz to 28 GHz, the carrier frequency is fixed at 4 GHz for all configurations. Thetransmit power \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{P}}_{\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e is given as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{\\text{P}}_{\\text{t}}\\left(\\text{d}\\text{B}\\text{m}\\right)=10{\\text{l}\\text{o}\\text{g}}_{10}\\left(\\text{P}\\text{o}\\text{w}\\text{e}\\text{r}\\left(\\text{W}\\right)\\right)+30$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eLet Tx power\u0026thinsp;=\u0026thinsp;25 Watt\u003c/p\u003e \u003cp\u003eThen \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{P}}_{\\text{t}}\\left(\\text{d}\\text{B}\\text{m}\\right)=10{\\text{l}\\text{o}\\text{g}}_{10}\\left(25\\:\\text{W}\\text{a}\\text{t}\\text{t}\\right)+30\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{P}}_{\\text{t}}\\left(\\text{d}\\text{B}\\text{m}\\right)\\)\u003c/span\u003e \u003c/span\u003e=44 dBm\u003c/p\u003e \u003cp\u003eNow the received signal power\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{P}}_{r}\\)\u003c/span\u003e\u003c/span\u003e at a distance d is given as:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\text{P}}_{\\text{r}}\\left(\\text{d}\\text{B}\\text{m}\\right)={\\text{P}}_{\\text{t}}+{\\text{G}}_{\\text{t}}+{\\text{G}}_{\\text{r}}-\\text{F}\\text{S}\\text{P}\\text{L}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mathbf{F}\\mathbf{S}\\mathbf{P}\\mathbf{L}\\left(\\mathbf{d}\\mathbf{B}\\right)=20{\\mathbf{l}\\mathbf{o}\\mathbf{g}}_{10}\\left(\\mathbf{d}\\right)+20{\\mathbf{l}\\mathbf{o}\\mathbf{g}}_{10}\\left(\\mathbf{f}\\right)-147.55\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eLet,\u003c/p\u003e \u003cp\u003edistance\u0026thinsp;=\u0026thinsp;200m\u003c/p\u003e \u003cp\u003efrequency\u0026thinsp;=\u0026thinsp;4Ghz= \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:4\\times\\:{10}^{9}\\)\u003c/span\u003e\u003c/span\u003e Hz\u003c/p\u003e \u003cp\u003etransmitter gain\u0026thinsp;=\u0026thinsp;0 dBi(isotropic)\u003c/p\u003e \u003cp\u003ereceiver gain\u0026thinsp;=\u0026thinsp;0 dBi(isotropic)\u003c/p\u003e \u003cp\u003etransmit power\u0026thinsp;=\u0026thinsp;44 dBm\u003c/p\u003e \u003cp\u003eThen,\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\text{F}\\text{S}\\text{P}\\text{L}\\left(\\text{d}\\text{B}\\right)=20{\\text{l}\\text{o}\\text{g}}_{10}\\left(200\\right)+20{\\text{l}\\text{o}\\text{g}}_{10}\\left(4\\times\\:{10}^{9}\\right)-147.55$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFSPL (dB)\u0026thinsp;=\u0026thinsp;90.51 dB\u003c/p\u003e \u003cp\u003eNow \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{P}}_{\\text{r}}\\left(\\text{d}\\text{B}\\text{m}\\right)=44+0+0-90.51=\\:-46.51\\:\\text{d}\\text{B}\\text{m}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e"},{"header":"III.\tMETHODOLOGY-PROPOSED PATH LOSS MODEL","content":"\u003cp\u003eA. \u003cem\u003eFree-Space Model\u003c/em\u003e\u003c/p\u003e \u003cp\u003eA basic model for wireless communication, the Free-Space Propagation Model (FSPM) describes how electromagnetic signals move across a perfect environment devoid of obstructions, reflection, scattering, and absorption. It provides an upper bound on performance under optimal circumstances and is used as a baseline model in simulation-based investigations of wireless systems, such as 4G and 5G networks. Understanding the optimal signal behaviour and separating the effects of system characteristics like frequency, antenna height, and transmit power are the main objectives of academic research and early-stage system evaluations, where this model is most helpful.The FSPL is the reduction in power density of a signal as it propagates through free space. The log-distance formula for FSPL in decibels (dB) is:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\mathbf{F}\\mathbf{S}\\mathbf{P}\\mathbf{L}\\left(\\mathbf{d}\\mathbf{B}\\right)=20{\\mathbf{l}\\mathbf{o}\\mathbf{g}}_{10}\\left(\\mathbf{d}\\right)+20{\\mathbf{l}\\mathbf{o}\\mathbf{g}}_{10}\\left(\\mathbf{f}\\right)-147.55$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAlternatively, in terms of wavelength \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\lambda\\:}\\)\u003c/span\u003e\u003c/span\u003e, the Friis Transmission Equation becomes:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\:\\:\\:\\mathbf{F}\\mathbf{S}\\mathbf{P}\\mathbf{L}\\left(\\mathbf{d}\\mathbf{B}\\right)=20{\\mathbf{l}\\mathbf{o}\\mathbf{g}}_{10}\\left(\\frac{4\\varvec{\\pi\\:}\\mathbf{d}}{\\varvec{\\lambda\\:}}\\right)$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\lambda\\:}\\)\u003c/span\u003e\u003c/span\u003e denotes wavelength of transmitted light in meters, f is the frequency in hertz, and d is the transmitter-to-receiver distance in meters.This demonstrates that FSPL rises with frequency and distance, indicating that attenuation is greater at higher frequencies (e.g., 4 GHz) than at lower ones within the same range [2].The received signal power \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{P}}_{\\text{r}}\\)\u003c/span\u003e\u003c/span\u003e at a distance d is given as:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Equ3\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\text{P}}_{\\text{r}}\\left(\\text{d}\\text{B}\\text{m}\\right)={\\text{P}}_{\\text{t}}+{\\text{G}}_{\\text{t}}+{\\text{G}}_{\\text{r}}-\\text{F}\\text{S}\\text{P}\\text{L}$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn this case, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{P}}_{\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e stands for transmit power in dBm, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{G}}_{\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e for transmitter antenna gain (dBi), and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{G}}_{\\text{r}}\\)\u003c/span\u003e\u003c/span\u003efor receiver antenna gain (dBi).For our simulation parameters the antenna gains are assumed to be zero (isotropic antennas) so:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Equ4\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:{\\:\\text{P}}_{\\text{r}}\\left(\\text{d}\\text{B}\\text{m}\\right)={\\text{P}}_{\\text{t}}-\\text{F}\\text{S}\\text{P}\\text{L}$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAn essential metric in wireless communication systems for assessing a network\u0026rsquo;s performance and quality is the Signal-to-Interference-plus-Noise Ratio (SINR). Interference is frequently overlooked while estimating the highest attainable SINR in the free-space model. The noise power is assumed to be main issue in this simplification. Thus, the SINR is defined as:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Equ5\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:\\text{S}\\text{I}\\text{N}\\text{R}=\\frac{{\\text{P}}_{\\text{s}}}{{\\text{P}}_{\\text{i}}+{\\text{P}}_{\\text{n}}}$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eHere, the SINR is a logarithmic indicator of signal quality and is given in decibels (dB). Where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{P}}_{\\text{s}}\\)\u003c/span\u003e\u003c/span\u003ethe power of the desired signal is,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{P}}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e is power of the interference of the signal and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{P}}_{\\text{n}}\\)\u003c/span\u003e\u003c/span\u003e is the power of background noise. Understanding the upper bounds of network performance under optimal circumstances is made easier by this approximation, which focuses on the difference between the received signal power and the noise power. But in practice, environmental influences and other cell interference can greatly affect SINR, therefore sophisticated methods like noise reduction and interference management are needed to maximize network efficiency.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eB. \u003cem\u003eClose-In Model\u003c/em\u003e\u003c/p\u003e \u003cp\u003eOne important and reliable model for analyzing large-scale signal loss in wireless channels is the Close-In (CI) Free Space Reference Distance Path Loss Model.It is especially proficient in showcasing signal performance throughout a varied frequency range, from sub-6 GHz to millimeter-wave (mmWave) bands, and has been recognized by 3GPPand IEEE for its effectiveness in modeling 5G scenarios in urban, suburban, and indoor environments. Introduced as a more straightforward option compared to the Alpha-Beta-Gamma (ABG) and floating intercept (FI) models, the CI model avoids the pitfalls of overfitting and ambiguous physical interpretation [12]. By linking the model to a specific physical reference\u0026mdash;the free space path loss at 1 meter\u0026mdash;it ensures both reliability and clarity in its application. The equation representing the CI model with a reference distance of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{d}_{0}\\)\u003c/span\u003e\u003c/span\u003e meters is articulated as:\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$\\:{\\text{P}\\text{L}}_{\\text{C}\\text{I}}\\left(\\text{f},\\text{d}\\right)=\\text{F}\\text{S}\\text{P}\\text{L}\\left(\\text{f},{\\text{d}}_{0}\\right)+10\\text{n}{\\text{l}\\text{o}\\text{g}}_{10}\\left(\\frac{\\text{d}}{{\\text{d}}_{0}}\\right)+{\\text{X}}_{{\\sigma\\:}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWhere n is the Path loss exponent, characterizing the rate of signal decay and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{\\sigma\\:}\\)\u003c/span\u003e\u003c/span\u003e is the Log-normal shadow fading (in dB).In contrast to the FSPL model, the CI path loss model takes into greater consideration the real-life conditions in which signals are transmitted. While the FSPL model considers conditions using the Friis transmission equation, it is based on ideal conditions like clear line of sight with no obstructions. Obstacles, multipath fading, and environmental changes are challenges neglected by the FSPL. The CI model addresses shortcomings of the FSPL model relay focuses on the distance range greater than the reference distance, typically 1 meter in our case, while also providing flexibility based on the environment through path loss exponent. Unlike CI, FSPL does not consider urban, rural suburbs, or indoors.\u003c/p\u003e \u003cp\u003eAlongside other innovations, CI has also built upon the foundation of shadow fading making it more effective at dealing with random signal fluctuations caused by environmental interruptions. CI however is quite effective when assuming NLOS conditions which are more frequent in everyday scenarios. In modern technology including 5G and mmWave communications which are more prone to high frequencies, CI has proven to work well as unlike other models, it maintains the dependence of frequency in its formulation[4\u0026ndash;5].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"IV.\t5G NETWORK COVERAGE PLANNING","content":"\u003cp\u003ePlanning for 5G network coverage involves the strategic design and installation of base stations, antennas, and associated infrastructure to guarantee optimal wireless signal reach, capacity, and quality. Unlike earlier generations, 5G presents new challenges due to its use of higher frequency bands like mmWave, smaller cell sizes, and advanced technologies such as beamforming and massive MIMO. Successful coverage planning is crucial to fully realize the advantages of 5G, consisting of massive machine-type communication (mMTC), ultra-reliable low latency communication (URLLC), and enhanced mobile broadband (eMBB). The fundamentals of network coverage are covered in this section, along with specifics of the thorough 5G planning, such as transmitter site design and network topology.\u003c/p\u003e \u003cp\u003e \u003cem\u003eA. Understanding Network Coverage\u003c/em\u003e \u003c/p\u003e \u003cp\u003eIn the realm of wireless communication, network coverage refers to the specific geographical areas where a wireless network can connect its users. This coverage is shaped by the location and capabilities of network infrastructure, including cell towers and antennas. The quality of coverage is impacted by elements such as signal strength, frequency bands, and environmental factors. With the rise of 5G technology, achieving superior coverage is essential for enabling rapid data transfer, low latency, and uninterrupted connectivity across various environments. Wireless communication often employs a cell-based structure, where each cell corresponds to a particular geographic area served by a base station that facilitates communication with mobile devices within its range. This design allows for efficient frequency reuse, minimizes interference, and maximizes coverage. Cell sizes can vary, with smaller cells typically found in densely populated urban areas and larger cells in rural settings. This cell structure is integral to the management of network resources, ensuring smooth transitions between cells, and sustaining reliable connectivity as users move through different regions. In 5G networks, enhancements such as small cells and beamforming technology are utilized to further improve coverage and capacity.\u003c/p\u003e \u003cp\u003e \u003cem\u003eB. Defining Network Layout\u003c/em\u003e \u003c/p\u003e \u003cp\u003eIn contemporary 5G networks, particularly in densely populated urban regions with high user demand and interference, cell sites employ sectorization rather than omnidirectional antennas. Each site is segmented into three 120-degree sectors, enhancing performance through directional signal transmission, minimized interference, and frequency reuse. In a 19-site hexagonal configuration, this results in 57 sectors (19 \u0026times; 3), ensuring effective coverage and smooth user transitions. Sectorization increases capacity by allowing each sector to utilize independent radio resources, accommodating more concurrent users [5]. Technologies such as massive MIMO and beamforming excel in this environment, enabling precise signal targeting for improved data rates and reduced interference. Furthermore, energy efficiency is improved, as sectors can dynamically adjust power based on traffic load, even shutting down during periods of low demand to conserve energy. Sectorization also streamlines network optimization, with adjustable parameters like antenna tilt and power per sector, ensuring robust performance in complex settings. In conclusion, a 57-sector, 19-site design is a strategic approach that optimizes coverage, capacity, efficiency, and spectral utilization, making it ideal for high-density 5G networks.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eC. \u003cem\u003eDesigning Transmitter Sites\u003c/em\u003e\u003c/p\u003e \u003cp\u003eThree transmitters can be installed at each cell location. An array of antenna elements or a single antenna element can be used to create transmitters. Given the usage of higher frequency bands (millimetre-wave bands) in 5G, the incorporation of numerous antennas into one arrayis feasible, for example, an 8-by-8 rectangular array with an operating frequency of 30 GHz, according to IMT-2020 [6].\u003c/p\u003e"},{"header":"V.\tSIMULATION PARAMETERS","content":"\u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eTABLE II Simulation Parameters For 5G Network Coverage And Sinr\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBandwidth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 Mhz\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBS Antenna gain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 dBi\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBS Antenna noise figure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 dB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceiver height\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceiver noise figure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7dB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceiver gain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 dBi\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of cell sites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"VI. RESULTS, DISCUSSION AND PERFORMANCE ANALYSIS","content":"\u003cp\u003eTo analyse the SINR (Signal-To-Interference-Noise-Ratio) iterations are made on antenna height, Inter-site distance and transmitter power by keeping the different parameters fixed for each case mentioned Table III. Recognizing that actual coverage can vary greatly depending on deployment location, this paper estimates 5G network coverage using the close in model and the free space propagation model. We have supplied the geographical latitude and longitude coordinates of the rectangle antenna that we use in our design. Bhubaneswar has been chosen as the venue. The transmitter is located at latitude 20.296059 N and longitude 85.824539 E. The different cases for the SINR simulations are depicted in Figs.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig20\" class=\"InternalRef\"\u003e10\u003c/span\u003e. The simulation complies with the IMT 2020 standards for planning and simulating coverage in 5G networks. A comprehensive list of all simulation parameters for different configurations can be found in Table III.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eTABLE III Parameter Configurations for 5G Urban Macrocell SINR Simulation Scenarios\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConfigA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConfig B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConfig C\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eConfig D\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBS antenna height\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10m-100m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInter-site distance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100m-500m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e200m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e200m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal transmit power\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44dBm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44dBm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30dBm-47dBm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44dBm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarrier frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4Ghz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4Ghz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4Ghz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9Ghz-28Ghz\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\u003eA. \u003cem\u003eResults For Free-Space Model\u003c/em\u003e\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates the SINR distribution within a 5G urban macrocell setting for antenna heights of 10 meters (left) and 100 meters (right), maintaining a consistent inter-site distance of 200 meters. At a height of 10 meters, the signal coverage is more concentrated, leading to higher SINR and reduced interference due to limited propagation and minimal overlap with adjacent cells [10]. However, this can result in coverage gaps in densely populated urban areas. Conversely, the 100-meter antenna height offers broader coverage but also increases inter-cell interference, which reduces the overall SINR. This comparison underscores the balance between signal quality and coverage when selecting antenna heights.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e6\u003c/span\u003e depicts a comparison of SINR distributions for two distinct inter-site distances (ISD) within a 5G urban macrocell setting, maintaining a constant base station antenna height of 25 meters. The left map depicts an ISD of 500 meters, while the right map illustrates an ISD of 100 meters. In the scenario with a 500 m ISD (left), coverage is less comprehensive, and SINR performance is diminished, particularly in outer regions such as Jayadev Vihar, due to the greater distance between base stations. As a result, signal strength is weaker, leading to potential performance issues for users located at the periphery. Conversely, the 100 m ISD scenario (right) reveals a marked enhancement in SINR across the area, including Jayadev Vihar, attributed to the closer placement of base stations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e7\u003c/span\u003e illustrate the effects of different transmit power levels in a 5G urban macrocell setting. The left image corresponds to a transmit power of 30 dBm, while the right image reflects a power level of 47 dBm, with all other parameters remaining unchanged. It is apparent that a higher transmit power significantly improves both signal quality and coverage area, as evidenced by the more extensive high SINR regions in the right image. In this scenario increasing the transmitter power might also increase interference to neighbouring cells, but this is often outweighed by the strong signal. The design of the SINR map, characterized by hexagonal patterns from the cell layout, will remain unchanged, but the colour gradients that reflect SINR values will be adjusted.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eB. \u003cem\u003eResult For Close-In Model\u003c/em\u003e\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e8\u003c/span\u003e illustrate the SINR distribution according to the Close-In model for antenna heights of 10 meters (left) and 100 meters (right). At 10 meters, the SINR is higher because of less interference and more concentrated coverage. However, at 100 meters, the wider coverage results in greater inter-cell interference, leading to a lower overall SINR across the region. Thus, by comparing the free space model and close in model we can clearly depicts that the coverage gaps are reduced in the SINR map and the SINR values are reduced due to large interference.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig19\" class=\"InternalRef\"\u003e9\u003c/span\u003e presents a comparison of SINR distributions for two different inter-site distances (ISD) in a close-in model, with the base station antenna consistently set at a height of 25 meters. The map on the left shows an ISD of 100 meters, whereas the map on the right displays an ISD of 500 meters. These maps suggest a similar analysis, indicating that coverage gaps are minimized compared to the free space model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig20\" class=\"InternalRef\"\u003e10\u003c/span\u003e is expected to undergo a similar analysis. Therefore, from the comparison above, we can confirm that the close-in model enhances coverage gaps, although it does so by diminishing signal strength and other parameters.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eC. \u003cem\u003eResult of Signal Strength for Free-space and Close-In Models\u003c/em\u003e\u003c/p\u003e \u003cp\u003eTable IV illustrate, the received signal strength (RSS) in dBm is displayed for various transmit power levels, contrasting the Free-Space and Close-In propagation models in Bhubaneswar. As the transmit power rises from 1W to 50W, both models demonstrate an increase in RSS, signifying stronger signals. The Free-Space model consistently provides higher RSS values compared to the Close-In model, which is due to its idealized assumptions that minimize environmental losses [11]. On the other hand, the Close-In model takes into account real-world urban barriers, resulting in greater path loss and diminished signal strength. For example, at a transmit power of 1W, the Free-Space model shows an RSS of -74.39 dBm, whereas the Close-In model reflects a lower \u0026minus;\u0026thinsp;90.76 dBm. Even at 50W, the Close-In model's RSS of -73.46 dBm is still less than the Free-Space model's -57.41 dBm. This indicates that the Free-Space model may overestimate signal strength, while the Close-In model provides a more realistic view of signal degradation in urban environments such as Bhubaneswar.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eTABLE IIIV Received Signal Strength (in dBm) vs Tx Power using Free Space and Close-In Models in Bhubaneswar\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePower (Watt)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFree-Space (dBm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClose-In(dBm)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-74.3973132323568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-90.7571545084454\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-67.4076131889966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-83.5292681390751\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-64.3973132323568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-80.5990250137152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-61.387013275717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-77.6838635506857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-57.4076131889966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-73.4620595914008\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\u003eTable IV demonstrates that in the urban setting of Bhubaneswar, increasing the height of antennas enhances signal strength, though the extent of improvement varies across different models. The free space model forecasts stronger signals, reaching \u0026minus;\u0026thinsp;58 dBm at a height of 10 meters. In contrast, the more realistic close-in model indicates weaker reception at -74 dBm at the same height, with smaller enhancements due to urban obstructions. The disparity between the models underscores the impact of Bhubaneswar's buildings and vegetation on signal degradation, emphasizing the importance of raising tower heights to 10\u0026ndash;100 meters to counteract real-world path loss.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eTABLE V Received Signal Strength (in dBm) vs Antenna Height using Free Space and Close-In Models in Bhubaneswar\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabe\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntenna Height (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFree-Space (dBm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClose-In(dBm)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-58.1914977032885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-74.1145716954686\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-58.1924812209569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-74.1526033111467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-58.1943076595538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-74.5491439742817\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-58.1969759560513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-74.4373383560491\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-58.2100140600042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-74.9830746535056\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\u003eTable V demonstratesthat higher frequencies exhibit a more drastic reduction in signal strength in real-world applications than theoretical models suggest for free space. The sub-6GHz bands (700MHz-3.5GHz) ensure better coverage, while mmWave frequencies (24GHz and above) encounter severe losses, limiting their feasibility for widespread deployment. In urban areas, signal strength decreases by 25-30dB per decade in close-in models, in contrast to a 20dB decrease in free space, with the performance gap increasing at higher frequencies.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eTABLE VI Received Signal Strength (in dBm) vs Carrier Frequency using Free Space and Close-In Models in Bhubaneswar\u003c/span\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabf\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarrier Frequency (Ghz)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFree-Space (dBm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClose-In(dBm)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-52.1723965050871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-61.1073046095007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-58.1929964183668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-75.6132366801209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-63.9689072033061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-97.5137072126748\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-81.387013275717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-329.708468687703\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-82.030706943145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-349.605752587733\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"VII. ANALYSIS OF CHALLENGES","content":"\u003cp\u003eThe Free Space Model is based on the premise of a direct line-of-sight (LOS) and the absence of obstacles, which makes it ideal for open spaces but problematic in urban environments. It often overestimates coverage since it overlooks the effects of reflection, diffraction, and scattering. In non-line-of-sight (NLOS) conditions, it predicts unrealistically high SINR values. Furthermore, it fails to consider shadowing and multipath phenomena that are significant in city landscapes, resulting in inaccuracies for scenarios involving street canyons, indoor users, or areas with obstructions.\u003c/p\u003e \u003cp\u003eThe Close-In model enhances realism by using a reference distance but has limitations: it's a single-parameter model that may oversimplify multi-slope urban path loss [14]. Accurate performance requires careful tuning of the path loss exponent (PLE) for specific environments (urban macro/micro, indoor). Additionally, it doesn't model fast fading and must be combined with stochastic models for complete channel characterization.\u003c/p\u003e \u003cp\u003eUrban SINR modelling presents significant challenges due to factors like multipath fading from closely packed infrastructure, shadowing effects, frequent non-line-of-sight (NLOS) scenarios, fluctuating interference patterns, and channel variations driven by mobility. To achieve accurate predictions in cities like Bhubaneswar, it is crucial to have detailed 3D antenna characteristics and extensive geospatial information, which are often not readily available. Consequently, advanced modelling strategies that include real-world calibration are required [15].\u003c/p\u003e"},{"header":"VIII. CONCLUSIONS AND FUTURE WORK","content":"\u003cp\u003eThis research illustrates the impact of antenna height, inter-site distance (ISD), transmission power, and propagation models on SINR and signal strength in 5G urban macrocells. Antennas positioned at lower heights (10 m) yield higher SINR but can lead to coverage gaps, whereas taller antennas (100 m) extend coverage but introduce more interference. Shorter ISDs (100 m) help reduce coverage gaps, facilitating dense urban deployment. The RSS analysis indicates that the Free-Space model tends to overestimate signal strength, while the Close-In model provides more accurate results for environments such as Bhubaneswar. These results underscore the importance of optimized deployment strategies and realistic modelling to achieve a balance between coverage and performance in urban 5G networks.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMIMO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMultiple\u0026ndash;input\u0026ndash;Multiple\u0026ndash;output\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSINR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSignal to Interference Noise Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFSPL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFree space path loss\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClose\u0026ndash;in model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLine of sight\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNLOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNon\u0026ndash;line of sight\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceived Signal Strength\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuality of Service\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e5G\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e5th Generation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eABG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlpha\u0026ndash;Beta\u0026ndash;Gamma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMMTC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMassive machine type communication\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eURLLC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUltra\u0026ndash;reliable low latency communication\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEMBB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEnhanced mobile broadband\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cul type=\"disc\"\u003e\n \u003cli\u003eAvailability of data and material\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;Not Applicable\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eCompeting interests\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;The authors declare that they have no competing interests\u0026quot;\u003c/em\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eFunding\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eAuthors\u0026apos; contributions\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003e\u0026quot;1st Author analyzed and done whole literature review. 2nd and 3rd was a major contributor in carrying out the work and writing the manuscript. All authors read and approved the final manuscript.\u0026quot;\u003c/em\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eAcknowledgements\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003e\u0026quot;Not applicable\u0026quot;\u003c/em\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCorgan, J., Nair, N., Bhattacharjea, R., Liu, W., Tadik, S., Tsou, T., \u0026amp; O\u0026apos;Shea, T. J. (2024). How critical is site-specific RAN optimization? 5G Open-RAN uplink air interface performance test and optimization from macro-cell CIR data. \u003cem\u003earXiv preprint arXiv:2410.19565\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eA. I. Zreikat and S. Mathew, \u0026ldquo;Performance evaluation and analysis of urban-suburban 5G cellular networks,\u0026rdquo; \u003cem\u003eComputers\u003c/em\u003e, vol. 13, no. 4, p. 108, 2024.\u003c/li\u003e\n\u003cli\u003eV. R. Farr\u0026eacute; Guijarro \u003cem\u003eet al\u003c/em\u003e., \u0026ldquo;Comparative evaluation of radio network planning for different 5G-NR channel models on urban macro environments in Quito city,\u0026rdquo; \u003cem\u003eIEEE Access\u003c/em\u003e, vol. 12, pp. 5721\u0026ndash;5732, 2024.\u003c/li\u003e\n\u003cli\u003eV. Tripathi and A. Ratnaparkahi, \u0026ldquo;SINR map study of 5G urban network,\u0026rdquo; Int. Res. J. Mod. Eng. Technol. Sci., vol. 6, no. 5, May 2024.\u003c/li\u003e\n\u003cli\u003eM. Bulti, \u0026ldquo;On the performance of multiple association with mobility support in 5G ultra-dense networks: Realistic network simulation,\u0026rdquo; \u003cem\u003eIET Communications\u003c/em\u003e, vol. 17, no. 11, pp. 1234\u0026ndash;1245, 2023.\u003c/li\u003e\n\u003cli\u003eA. Tripathi, P. K. Tiwari, S. Prakash, and N. K. Shukla, \u0026ldquo;MIMO channel modeling and cell size optimization for 5G dense urban macro cell environment,\u0026rdquo; \u003cem\u003ePreprint\u003c/em\u003e, Dec. 2022.\u003c/li\u003e\n\u003cli\u003eS. K. Khan \u003cem\u003eet al\u003c/em\u003e., \u0026ldquo;UAV-aided 5G Network in Suburban, Urban, Dense Urban, and High-rise Urban Environments,\u0026rdquo; in \u003cem\u003eProc. 2020 IEEE 19th Int. Symp. Network Computing and Applications (NCA)\u003c/em\u003e, Cambridge, MA, USA, Nov. 2020, pp. 1\u0026ndash;4.\u003c/li\u003e\n\u003cli\u003eEricsson Technological Review. (2020). 5G evolution: 3GPP releases 16 \u0026amp; 17 overview. [Online] Available: https://www.ericsson.com/en/reports-and-papers/ericsson-technology-review/articles/5g-nr evolution.\u003c/li\u003e\n\u003cli\u003eEricsson. (2020). 5G Wireless Access: An Overview. 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Mangaraj, \u0026ldquo;Antenna array beam scanning and SINR visualization on a map for 5G urban macro-cell testenvironment,\u0026rdquo; Dept. of Electronics and Telecommunication Engineering, Veer Surendra Sai University of Technology, Burla Sambalpur, Odisha, India.\u003c/li\u003e\n\u003cli\u003eReport ITU-R M.[IMT-2020.EVAL], \u0026quot;Guidelines for evaluation of radio interface technologies for IMT-2020\u0026quot;, 2017. https://www.itu.int/md/R15-SG05-C-0057\u003c/li\u003e\n\u003cli\u003eSun, S.,Rapport, T.S., Thomas, T., Ghosh, A., Nguyen, H., Kovacs, I., Rodriguez, I., Koymen, O.,andPrartyka, A. \u0026quot;Investigation of prediction accuracy, sensitivity, and parameter stability of large-scale propagation path loss models for 5G wireless communications.\u0026quot; IEEE Transactions on Vehicular Technology, Vol 65, No.5, pp.2843-2860, May 2016.\u003c/li\u003e\n\u003cli\u003eA. Gupta and R. K. Jha, A survey of 5G network: architecture and emerging technologies, IEEE Access, vol. 3 (2015), pp. 1206-1\u003c/li\u003e\n\u003cli\u003eReport ITU-R M.2135-1, \u0026quot;Guidelines for evaluation of radio interface technologies for IMT-Advanced\u0026quot;, 2009. https://www.itu.int/dms_pub/itu-r/opb/rep/R-REP-M.2135-1-2009-PDF-E.pdf\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"5G Cellular Network Deployment, Coverage, Interference Management, Network planning, Signal Strength","lastPublishedDoi":"10.21203/rs.3.rs-6570203/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6570203/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis research assesses the performance of Signal-to-Interference-plus-Noise Ratio (SINR) within urban macro-cells of 5G, comparing two different path loss models\u0026mdash;Free-Space Propagation Model (FSPM) and Close-In (CI) model\u0026mdash;to evaluate their effects on signal quality and network dependability. Concentrating on densely populated urban areas in Bhubaneswar, India, the study utilizes a hexagonal cell setup with 19 sites to investigate how crucial factors such as antenna height, inter-site distance, and transmission power affect SINR distribution. The simulations demonstrate that the CI model, which accounts for practical propagation effects such as shadowing and multipath fading, offers more accurate SINR predictions than the idealized FSPM. The results suggest that varying antenna heights enhances SINR by diminishing interference. The RSS analysis reveals that the Free-Space model often predicts higher signal strength than actual, whereas the Close-In model yields more precise outcomes for given environments. These findings highlight the necessity of refined deployment strategies and realistic modelling to strike a balance between coverage and performance in urban 5G networks.\u003c/p\u003e","manuscriptTitle":"Performance Evaluation of SINR in 5G Urban Macro-Cells with Variable Parameters Under different path loss models","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-15 07:34:58","doi":"10.21203/rs.3.rs-6570203/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":"c8637cf9-1054-41c8-a25a-d404fe70ba04","owner":[],"postedDate":"May 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-16T06:11:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-15 07:34:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6570203","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6570203","identity":"rs-6570203","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}