Performance Enhancement through Optimal Power Allocation for Downlink PD-NOMA-UFMC in 5G Networks using Genetic algorithm and MFO

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Power Domain Non-Orthogonal Multiple Access (PD-NOMA) combined with Universal Filtered Multi-Carrier (UFMC) has emerged as a promising solution to address these needs. This combination aims to enhance spectral efficiency, capacity, and user fairness in 5G networks by capitalizing on the strengths of both PD-NOMA and UFMC. Nevertheless, the efficient allocation of power among users poses an optimization challenge within the confines of a specified power budget to achieve optimal performance in PD-NOMA-UFMC systems. This paper presents a power optimization methodology for PD-NOMA-UFMC utilizing intelligent optimization techniques, namely Genetic Algorithm (GA) and Moth Flame Optimization (MFO) algorithm. The proposed approach aims to maximize the overall system throughput by dynamically assigning power levels to users based on their individual channel conditions. Simulation results showcase the effectiveness of the power optimization technique employing GA and MFO in enhancing the performance of PD-NOMA-UFMC systems in terms of throughput, fairness, and power efficiency. Additionally, the paper discusses comparative results of optimization using GA and MFO. 5G PD-NOMA UFMC GA MFO Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1.0 Introduction In recent times, the landscape of wireless communication systems has witnessed significant expansion, driven by escalating demands for enhanced data rates, heightened reliability, and seamless connectivity. Noteworthy emphasis has been placed on pivotal technologies underpinning 5G and Beyond 5G systems, encompassing small cells, device-centric architectures, beamforming, full-duplex technology, massive multiple-input multiple-output (MIMO), millimeter (mm), and terahertz (THz) waves, non-orthogonal multiple access (NOMA), and reconfigurable intelligent surfaces (RIS). Notably, the bandwidth potential offered by millimeter waves surpasses tenfold the cumulative bandwidth of the entire 4G cellular spectrum [ 1 ]. The advent of 5G communication systems has catalysed an upsurge in demand for efficient multiple access (MA) techniques capable of sustaining high data rates and accommodating burgeoning user populations [ 2 ]. Across successive generations of mobile communication, MA strategies empower users to concurrently leverage time and/or spectrum resources while preserving the quality-of-service (QoS) they receive. These strategies encompass a spectrum of approaches, including frequency-division multiple access (FDMA), time-division multiple access (TDMA), orthogonal frequency-division multiple access (OFDMA), code-division multiple access (CDMA), and non-orthogonal multiple access (NOMA), among others, deployed across different wireless communication epochs to allocate spectra to diverse users. Figure 1 provides a comparative delineation of distinct MA techniques across varied wireless generations [ 2 – 4 ]. Orthogonal frequency-division multiplexing (OFDM) has long been the preferred modulation scheme due to its resilience against frequency-selective fading channels. However, OFDM faces challenges like high out-of-band emissions and suboptimal spectral efficiency, particularly in scenarios with numerous users and limited spectrum availability. To address these challenges, various non-orthogonal multiple access (NOMA) schemes have been proposed, categorized into power-domain (PD) NOMA and code-domain (CD) NOMA. Unlike traditional approaches in 3G and 4G cellular networks, NOMA eliminates orthogonality, enabling multiple users to share frequency and time resources within the same spatial layer through power or code domain multiplexing. The concept of power-domain NOMA was introduced by Y. Saito et al [ 5 ]. PD-NOMA, a prominent multiple access technique in 5G networks, enables multiple users to utilize the same radio resource in the power domain. Resource allocation is critical in PD-NOMA, involving the optimal assignment of available radio resources to users. Various studies have proposed different resource allocation schemes for PD-NOMA. For example, authors in [ 5 ] suggested a joint power and subcarrier allocation scheme maximizing the sum rate while meeting users' quality of service (QoS) requirements. Similarly, [ 6 ] proposed a user grouping and resource allocation scheme considering both channel quality and QoS requirements to enhance PD-NOMA performance. In PD-NOMA, users are segregated based on their power levels, with each user assigned a unique power level corresponding to their channel quality. Users with superior channel conditions receive lower power levels, while those with inferior conditions are allocated higher power levels. Various researchers, including Y. Saito et al. [ 5 ], Z. Ding et al. [ 6 ], have explored the implementation of power-domain NOMA. Additionally, the spectral efficiency gains of NOMA have been well established. Furthermore, recent works have introduced innovative concepts to enhance device connectivity in NOMA systems. For instance, Z. Yuan et al. [ 7 ] proposed methods to increase the number of connected devices, while Saito et al. [ 17 ] demonstrated the applicability of basic NOMA with a Successive Interference Cancellation (SIC) receiver in the uplink. Linglong Dai et al. [ 8 ] compared various NOMA schemes for 5G and suggested that the number of connected devices could be increased at the expense of receiver complexity. Hence, NOMA emerges as a promising choice to achieve higher spectral efficiency and facilitate massive connectivity in 5G networks. Power Domain Non-Orthogonal Multiple Access (PD-NOMA) emerges as a promising approach for 5G networks, offering potential improvements in overall system capacity and user quality of service, especially in densely populated urban areas with high data traffic demands. In the framework of PD-NOMA, key functions at the transmitter end involve power allocation and superposition coding, while Successive Interference Cancellation (SIC) plays a crucial role at the receiver end. Let's consider a scenario of downlink transmission from the Base Station (BS) to two separate users, where d1 and d2 denote the distances of these users from the BS. The BS transmits two distinct messages, x1 and x2, intended for user 1 (far user) and user 2 (near user) respectively. Power allocation factors α1 and α2 (with α1 + α2 = 1) are assigned to user 1 and user 2, while fading coefficients h1 and h2 characterize the channel from the BS to each user. To ensure fairness among users, a strategy known as fair power allocation (PA), based on channel state information (CSI), was introduced [ 9 – 10 ]. In case of multiple user, many time, the fair power allocation scheme is not optimum due to dynamics of wireless channels. After the superposition coding, transmitted NOMA signal by the BS is, $$x=\sqrt P (\sqrt {{\alpha _1}} {x_1}+\sqrt {{\alpha _2}} {x_2})$$ 1 Where P is the transmitted power received at the user1 is given by $$y1=h1*x+w1$$ 2 The received signal at the user2 after propagating through Rayleigh fading channel h2 is given by $$y2=h2*x+w2$$ 3 Where w1 and w2 represent white Gaussian noises. The signals y1 and y2 are the received signals. The message signals are extracted using the SIC, followed by the calculation of the BER. The BER is a function of signal power and fading through the channel. The scheme of SIC is shown in Fig. 3 . The capacity achieved by SIC for 2 users in PD-NOMA is depends on power assigned and the wireless channel. For far user the capacity R f is given by [ 11 ] \({R}_{f}={log}_{2}(1+\frac{{\left|{h}_{f}\right|}^{2}P{\alpha }_{f}}{{\left|{h}_{f}\right|}^{2}P{\alpha }_{n}+{\sigma }^{2}}\) ) (4) Where P is the total power. Normally higher power (P. α f ) assigned to far user compensate the attenuation of the wireless channel. For near user the capacity R n is given by \({R}_{n}={log}_{2}(1+\frac{{\left|{h}_{n}\right|}^{2}P{\alpha }_{n}}{{\sigma }^{2}}\) ) (5) Where, α f and α n are the power allocation coefficient for far and near user respectively. The sum of these coefficients is 1. Hence the optimum power allocation is essential for faithful communication. In this paper, PD-UFMC with optimum power allocation is simulated for performance analysis. 2. Universal Filtered Multi-Carrier (UFMC) Several multicarrier techniques are emerging as strong candidates for 5G waveforms, aiming to improve spectrum efficiency beyond what OFDM can achieve. The primary design objective for 5G waveforms is their flexibility and scalability to cater to various applications across diverse and heterogeneous domains. A thorough review and analysis of major waveform contenders for 5G, including Generalized Frequency Division Multiplexing (GFDM), Filter Bank Multicarrier (FBMC), Filtered-OFDM (F-OFDM), and Universal Filtered Multi-Carrier (UFMC), have been conducted. GFDM and FBMC, leveraging subcarrier filtering, offer robustness against Inter-Symbol Interference (ISI). However, they require a completely new transceiver design, lacking backward compatibility with 4G-LTE [ 12 – 14 ]. To address these limitations, researchers have explored alternative modulation techniques, favouring sub-band filtering over subcarrier-wise filtering for flexibility. UFMC, employing sub-band filtering where each sub-band covers consecutive subcarriers, emerges as a promising technique. UFMC combines the strengths of both OFDM and single-carrier modulation. At the transmitter, a bank of sub-filters shapes the spectrum and mitigates interference, achieving greater spectral efficiency without a cyclic prefix (CP) and reducing out-of-band emissions. Additionally, UFMC demonstrates resilience against frequency-selective fading channels, making it suitable for challenging channel conditions. The mathematical model governing UFMC encompasses signal representation, sub-filter design, subcarrier modulation, sub-filtering, channel modelling, receiver processing, subcarrier demodulation, equalization, and data decoding [ 15 ]. A UFMC system with N subcarriers and L sub-filters is examined. The baseband input signal, x(t), is band-limited with a bandwidth of B and sampled at a rate of fs = N/T, where T is the symbol duration. L sub-filters shape the spectral response of the transmitted signal, with each sub-filter designed to overlap frequency responses for desired shaping. Subcarriers, modulated by complex-valued symbols from a modulation alphabet, are equally spaced in frequency. After modulation, subcarriers are convolved with corresponding sub-filters, and their outputs are summed to yield the UFMC signal, z(t). At the receiver, the received signal undergoes matched filtering matched to transmitter sub-filters to compensate for inter symbol interference (ISI). The demodulated symbols are then equalized to mitigate channel effects and decoded to recover transmitted data [ 16 – 17 ]. 3. Power Domain UFMC Power Domain Non-Orthogonal Multiple Access (PD-NOMA) emerges as a promising Radio Access Technique (RAT) for the forthcoming generation of wireless communication systems. It facilitates the support of multiple users by leveraging frequency domain overloading, while assigning distinct power allocations from the overall available transmit power pool. Through the formation of user groups exhibiting significant Signal to Interference Noise Ratio (SINR) disparities, PD-NOMA effectively boosts spectral efficiency. To further optimize this system, Universal Filtered Multi-Carrier (UFMC), a versatile OFDM waveform, proves advantageous. UFMC not only offers flexibility but also mitigates the necessity for stringent synchronization requirements [ 16 ]. Expanding upon UFMC, Power Domain UFMC (PD-UFMC) is introduced, wherein subcarriers are organized into multiple power domains, with each domain assigned a specific power level. This configuration enables more efficient power allocation, interference management, and overall system performance optimization. PD-UFMC possesses the capability to dynamically allocate power to different domains based on channel conditions, user requirements, and quality-of-service (QoS) constraints, thereby enhancing adaptability and resource utilization [ 18 ]. The power spectral density PD-NOMA system with K users sharing the same time-frequency resource block. The users are indexed from k = 1 to K. The available power is divided into J power domains, denoted as P1, P2, ..., PJ, where J ≤ K. Each power domain represents a portion of the total available power. The channel between the transmitter and each user is represented by h k , which is the channel coefficient for user k. The channel coefficients can be complex-valued, representing both magnitude and phase of the channel gain. The power allocation in PD-NOMA involves assigning power levels to each user within the available power domains. Let pk represent the power allocated to user k, where pk∈ {P1, P2, ..., PJ}. The power allocation is subject to power constraints and user-specific quality-of-service (QoS) requirements. The power spectrum for two users is shown in Fig. 5 . The receiver plays a crucial role in PD-NOMA systems by executing detection and decoding tasks to recover transmitted symbols from all users. Several detection techniques, including successive interference cancellation (SIC), maximum likelihood (ML) detection, or minimum mean square error (MMSE) detection, can be employed. Decoding involves making decisions based on the detected information regarding the received symbols. In PD-NOMA, effective interference management is essential for successful signal decoding from different users. Techniques such as SIC are utilized to mitigate interference from signals detected from users with higher power levels, thereby aiding in detecting signals from users with lower power levels. Both power allocation and decoding processes must consider the Quality of Service (QoS) requirements of users. QoS metrics, such as signal-to-interference-plus-noise ratio (SINR), error rate, and throughput, play a vital role in evaluating the service quality delivered to each user. The power allocation scheme should aim to optimize overall system throughput while fulfilling the QoS needs of individual users. Efficient power allocation is critical to unleashing the full potential of PD-NOMA and maximizing system throughput. This paper tackles the power optimization challenge in PD-NOMA systems by leveraging Genetic Algorithm (GA) and Moth Flame Optimization (MFO) algorithm. 4. Proposed scheme for power optimization for PD-NOMA using GA and MFO This section presents the power optimization framework for PD-NOMA using the GA and MFO algorithm. A mathematical model for the objective function in power optimization for power-domain non-orthogonal multiple access (PDNOMA), we need to consider the specific optimization goals and constraints of the PDNOMA system. The objective function typically aims to minimize BER. N: Total number of users in the PDNOMA system Pe i : BER of user i, where i = 1, 2, ...., N h i : Channel gain of user i w i : Weighting factor for user i. Now, it can formulate the objective function for power optimization in PD-NOMA as follows: Minimize: \(\sum _{i=1}^{N}{w}_{i}*{Pe}_{i}\) 4.1 GA based Optimization Genetic Algorithms (GAs) represent a category of evolutionary algorithms inspired by natural selection processes. They find extensive application in optimization scenarios characterized by large and intricate search spaces. GAs operate on a population of potential solutions, employing genetic operators such as selection, crossover, and mutation to iteratively generate new solution generations [ 19 – 20 ]. Optimizing Power Domain Non-Orthogonal Multiple Access (PD-NOMA) through Genetic Algorithms (GA) stands as a crucial endeavor for future wireless communication systems. PD-NOMA facilitates multiple users to share the same time-frequency resources within a wireless communication setup by allocating them to distinct power domains. Given the complexity of optimization tasks, particularly in power allocation for PD-NOMA, GA algorithms prove adept at tackling such challenges. The paper addresses a specific instance involving two users, which can be extrapolated to encompass multi-user scenarios as well.4.2 Moth Flame Optimization (MFO) based Optimization The Moth Flame Optimization (MFO) algorithm emerges as a metaheuristic optimization approach tailored for power allocation tasks within PD-NOMA systems. The paper elucidates the fundamental principles and procedural steps of the MFO algorithm, encompassing initialization, reproduction, attraction, and exploration phases. Leveraging the behavior of moths in search of optimal solutions, MFO is adept at addressing intricate optimization challenges, notably power allocation in PD-NOMA [ 21 – 22 ]. In contrast to Genetic Algorithms (GA), MFO demonstrates superior convergence rates. Consequently, the paper provides a comparative analysis between the two methodologies, shedding light on their respective efficacies in the context of PD-NOMA power allocation.5.0 Performance Evaluation and Comparisons The simulation environment has been developed to evaluate the BER performance of the PD-NOMA system. The simulation parameters for evaluating the power optimization by GA and MFO are given in Table 1 : Table 1 Simulation Parameters Parameters Value Modulation QAM16 Channel Coding Convolution code, rate = 1/2 FFT Size 512 Subband Size 20 Number of subband 10 Filter type Dolph-Chebyshev window Filter length 43 side-lobe attenuation, dB 40 No. of Users 2 SNR range 0 to 35 dB Wireless Channel Rayleigh Channel Method for interference c cancellation SIC The performance analysis has been performed by GA and MFO separately the results of optimization and their comparison are given ahead. GA based optimization : The performance of the proposed GA-based power optimization technique for PD-NOMA is evaluated through simulations. The parameters of GA are as follows, No. of Generation: 25 Population Size: 15 Elite count :2 The convergence of the optimization has been achieved within 12 iterations. The conversion curve showing the best fitness value is given in the Fig. 6 The normalized power spectrum of near and far user with GA based optimization is shown in the Fig. 7 . The bit error rate for different SNR value for 16-QAM has been evaluated and shown in the Fig. 8 . The performance of the near user and far user are optimized with equal weight. w1 and w2 are considered 0.5 for optimization. The performance of the near user is better than the far user which is obvious in case of the large difference between the distance of far user from base station as compared to the near user. MFO base optimization : The performance of the proposed MFO-based power optimization technique for PD-NOMA is evaluated through simulations. The parameters of MFO are as follows, No. of Iteration: 40 Population Size: 15 Constant for defining the shape of the logarithmic spiral (b) :1 The Convergence Curve showing the stable fitness value after iteration number 24 and considered the best optimum value of the normalized power for near and far user. The power spectrum of the users with this optimization is shown in Fig. 7 . Similar to the GA based optimization, the bit error rate for different SNR value for 16-QAM has been evaluated and shown in the Fig. 10 . In this case also, w1 and w2 are considered 0.5 for optimization. The paper compares the results with power allocation schemes using GA and MFO based power allocation. The performance metrics include system BER, and power efficiency. The simulation results demonstrate the superiority of the MFO-based approach in achieving higher throughput, improved fairness among users, and efficient power utilization. Table 2 Comparative result of GA and MFO based optimization S. No. Optimization with GA Optimization with MFO Power to NEAR user 0.1949 0.199 Power to FAR user 0.8051 0.801 Fitness Value 0.01138 0.0117 Number of iteration for convergence 12 24 SNR at BER 0.01 for near user 7 dB 6 dB SNR at BER 0.01 for FAR user 14 dB 11 dB Conclusions Performance evaluations of UFMC for 5G are conducted through simulations and comparisons with other modulation techniques. The evaluations focus on key performance metrics such as spectral efficiency, error rates, and robustness to interference. The results demonstrate the superior performance of UFMC in various scenarios, including high-mobility environments, dense networks, and multi-user scenarios. Comparative studies with OFDM and other modulation schemes highlight the advantages of UFMC in terms of spectral efficiency, interference resilience, and overall system capacity. To assess the performance of PD-UFMC, extensive simulations and comparisons with existing modulation schemes are conducted. The evaluations focus on key performance metrics such as spectral efficiency, error rates, and energy efficiency. The results demonstrate the superiority of PD-UFMC over conventional OFDM and UFMC in scenarios with various channel conditions and interference levels. Furthermore, comparisons with other advanced modulation schemes, such as non-orthogonal multiple access (NOMA) Declarations Author Contribution All co-authors reviewed the manuscript and provide valuable inputs for implemetation of simulation codes . Data Availability Data is provided within the manuscript . References Narayanan, A., Rochman, M. I., Hassan, A., Firmansyah, B. S., Sathya, V., Ghosh, M., Qian, F., & Zhang, Z. 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Optimal Power Flow using the Moth Flam Optimizer: A case study of the Algerian power system. Indonesian Journal of Electrical Engineering and Computer Science , 1 (3), 431–445. Additional Declarations No competing interests reported. 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-4357764","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":304140906,"identity":"e901bd66-1c10-4e9c-ac34-493dd250f812","order_by":0,"name":"Gopal K 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RGPV","correspondingAuthor":false,"prefix":"","firstName":"Vineeta","middleName":"Saxena","lastName":"Nigam","suffix":""},{"id":304140908,"identity":"ff2a17c6-f201-4ee4-b396-7e8a58b14204","order_by":2,"name":"Rakesh K Arya","email":"","orcid":"","institution":"UIT, RGPV","correspondingAuthor":false,"prefix":"","firstName":"Rakesh","middleName":"K","lastName":"Arya","suffix":""}],"badges":[],"createdAt":"2024-05-02 08:32:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4357764/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4357764/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56888554,"identity":"05d901b1-18cd-4152-bd7b-90874c6a74de","added_by":"auto","created_at":"2024-05-21 19:02:48","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":40653,"visible":true,"origin":"","legend":"\u003cp\u003eMultiple accesses techniques in different wireless generations\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4357764/v1/8873cc517512e5e7025ad5d3.jpg"},{"id":56888550,"identity":"13da8c1d-325f-461f-9292-6d7bcc80afdd","added_by":"auto","created_at":"2024-05-21 19:02:47","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":27146,"visible":true,"origin":"","legend":"\u003cp\u003eScheme of PDNOMA system\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4357764/v1/69b1ae179bfb59a849e2206a.jpg"},{"id":56889569,"identity":"dcb45199-fcff-407b-9d68-3c084b07c0ae","added_by":"auto","created_at":"2024-05-21 19:10:48","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":38030,"visible":true,"origin":"","legend":"\u003cp\u003eSIC scheme for PDNOMA\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4357764/v1/47f6bfd7981a555aacd5e7b3.jpg"},{"id":56888560,"identity":"380ad555-e604-493d-8418-7647600b2441","added_by":"auto","created_at":"2024-05-21 19:02:48","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":41030,"visible":true,"origin":"","legend":"\u003cp\u003eScheme of Power domain UFMC\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4357764/v1/7d9cc571add6ebcd517e56ab.jpg"},{"id":56888551,"identity":"300e6f14-095a-438b-b4b8-7996c5517161","added_by":"auto","created_at":"2024-05-21 19:02:47","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":22981,"visible":true,"origin":"","legend":"\u003cp\u003ePower spectral density of two users for Power domain UFMC\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4357764/v1/cf34731f70d3ecd14c5d358a.jpg"},{"id":56889568,"identity":"c430f333-3e39-4fbb-a97f-f0ed16b75d25","added_by":"auto","created_at":"2024-05-21 19:10:47","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":14467,"visible":true,"origin":"","legend":"\u003cp\u003eConvergence Curve of GA based power allocation\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4357764/v1/cca789906790908c67fe4416.jpg"},{"id":56888556,"identity":"535ed30e-7c57-4591-81c4-bd80b7cc732a","added_by":"auto","created_at":"2024-05-21 19:02:48","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":28480,"visible":true,"origin":"","legend":"\u003cp\u003ePower spectral density with GA based power allocation\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4357764/v1/d22602da5465a78cadcdd9c1.jpg"},{"id":56888555,"identity":"a3d7866a-83c5-49e8-8ffe-eaadcc4c7cfc","added_by":"auto","created_at":"2024-05-21 19:02:48","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":34070,"visible":true,"origin":"","legend":"\u003cp\u003eBER performance of PDNOMA with GA based power allocation\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4357764/v1/b0fc79b414796dd42e13bb0b.jpg"},{"id":56889876,"identity":"cc640eba-5e2f-4228-ab7b-126c1bcd2bd4","added_by":"auto","created_at":"2024-05-21 19:18:48","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":21064,"visible":true,"origin":"","legend":"\u003cp\u003eConvergence Curve of MFO based power optimization.\u003c/p\u003e","description":"","filename":"Picture9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4357764/v1/f2a41cdf1a4ae0c90a8e9e82.jpg"},{"id":56888559,"identity":"82a8b555-d104-4beb-abf5-a0d1a178c270","added_by":"auto","created_at":"2024-05-21 19:02:48","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":24330,"visible":true,"origin":"","legend":"\u003cp\u003ePower spectral density with GA based power allocation\u003c/p\u003e","description":"","filename":"Picture10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4357764/v1/e4217f85693034d8c78189c6.jpg"},{"id":56889570,"identity":"8ddb4bd5-f1b9-4fa7-8f8d-32f50010304b","added_by":"auto","created_at":"2024-05-21 19:10:48","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":13586,"visible":true,"origin":"","legend":"\u003cp\u003eBER performance of PDNOMA with GA based power allocation\u003c/p\u003e","description":"","filename":"Picture11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4357764/v1/7c1c5e7638977c06309aec06.jpg"},{"id":81741337,"identity":"cde20fda-44b2-4cc2-8697-7f13c16360a0","added_by":"auto","created_at":"2025-05-01 01:46:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":812777,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4357764/v1/3209e0bd-54f2-49ef-8087-11231c829d98.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Performance Enhancement through Optimal Power Allocation for Downlink PD-NOMA-UFMC in 5G Networks using Genetic algorithm and MFO","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eIn recent times, the landscape of wireless communication systems has witnessed significant expansion, driven by escalating demands for enhanced data rates, heightened reliability, and seamless connectivity. Noteworthy emphasis has been placed on pivotal technologies underpinning 5G and Beyond 5G systems, encompassing small cells, device-centric architectures, beamforming, full-duplex technology, massive multiple-input multiple-output (MIMO), millimeter (mm), and terahertz (THz) waves, non-orthogonal multiple access (NOMA), and reconfigurable intelligent surfaces (RIS). Notably, the bandwidth potential offered by millimeter waves surpasses tenfold the cumulative bandwidth of the entire 4G cellular spectrum [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe advent of 5G communication systems has catalysed an upsurge in demand for efficient multiple access (MA) techniques capable of sustaining high data rates and accommodating burgeoning user populations [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Across successive generations of mobile communication, MA strategies empower users to concurrently leverage time and/or spectrum resources while preserving the quality-of-service (QoS) they receive. These strategies encompass a spectrum of approaches, including frequency-division multiple access (FDMA), time-division multiple access (TDMA), orthogonal frequency-division multiple access (OFDMA), code-division multiple access (CDMA), and non-orthogonal multiple access (NOMA), among others, deployed across different wireless communication epochs to allocate spectra to diverse users. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides a comparative delineation of distinct MA techniques across varied wireless generations [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOrthogonal frequency-division multiplexing (OFDM) has long been the preferred modulation scheme due to its resilience against frequency-selective fading channels. However, OFDM faces challenges like high out-of-band emissions and suboptimal spectral efficiency, particularly in scenarios with numerous users and limited spectrum availability. To address these challenges, various non-orthogonal multiple access (NOMA) schemes have been proposed, categorized into power-domain (PD) NOMA and code-domain (CD) NOMA. Unlike traditional approaches in 3G and 4G cellular networks, NOMA eliminates orthogonality, enabling multiple users to share frequency and time resources within the same spatial layer through power or code domain multiplexing. The concept of power-domain NOMA was introduced by Y. Saito et al [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePD-NOMA, a prominent multiple access technique in 5G networks, enables multiple users to utilize the same radio resource in the power domain. Resource allocation is critical in PD-NOMA, involving the optimal assignment of available radio resources to users. Various studies have proposed different resource allocation schemes for PD-NOMA. For example, authors in [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] suggested a joint power and subcarrier allocation scheme maximizing the sum rate while meeting users' quality of service (QoS) requirements. Similarly, [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] proposed a user grouping and resource allocation scheme considering both channel quality and QoS requirements to enhance PD-NOMA performance.\u003c/p\u003e \u003cp\u003eIn PD-NOMA, users are segregated based on their power levels, with each user assigned a unique power level corresponding to their channel quality. Users with superior channel conditions receive lower power levels, while those with inferior conditions are allocated higher power levels. Various researchers, including Y. Saito et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], Z. Ding et al. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], have explored the implementation of power-domain NOMA. Additionally, the spectral efficiency gains of NOMA have been well established.\u003c/p\u003e \u003cp\u003eFurthermore, recent works have introduced innovative concepts to enhance device connectivity in NOMA systems. For instance, Z. Yuan et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] proposed methods to increase the number of connected devices, while Saito et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] demonstrated the applicability of basic NOMA with a Successive Interference Cancellation (SIC) receiver in the uplink. Linglong Dai et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] compared various NOMA schemes for 5G and suggested that the number of connected devices could be increased at the expense of receiver complexity. Hence, NOMA emerges as a promising choice to achieve higher spectral efficiency and facilitate massive connectivity in 5G networks.\u003c/p\u003e \u003cp\u003ePower Domain Non-Orthogonal Multiple Access (PD-NOMA) emerges as a promising approach for 5G networks, offering potential improvements in overall system capacity and user quality of service, especially in densely populated urban areas with high data traffic demands. In the framework of PD-NOMA, key functions at the transmitter end involve power allocation and superposition coding, while Successive Interference Cancellation (SIC) plays a crucial role at the receiver end. Let's consider a scenario of downlink transmission from the Base Station (BS) to two separate users, where d1 and d2 denote the distances of these users from the BS. The BS transmits two distinct messages, x1 and x2, intended for user 1 (far user) and user 2 (near user) respectively. Power allocation factors α1 and α2 (with α1\u0026thinsp;+\u0026thinsp;α2\u0026thinsp;=\u0026thinsp;1) are assigned to user 1 and user 2, while fading coefficients h1 and h2 characterize the channel from the BS to each user. To ensure fairness among users, a strategy known as fair power allocation (PA), based on channel state information (CSI), was introduced [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In case of multiple user, many time, the fair power allocation scheme is not optimum due to dynamics of wireless channels.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAfter the superposition coding, transmitted NOMA signal by the BS is,\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$x=\\sqrt P (\\sqrt {{\\alpha _1}} {x_1}+\\sqrt {{\\alpha _2}} {x_2})$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhere P is the transmitted power received at the user1 is given by\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$y1=h1*x+w1$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe received signal at the user2 after propagating through Rayleigh fading channel h2 is given by\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$y2=h2*x+w2$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere w1 and w2 represent white Gaussian noises. The signals y1 and y2 are the received signals. The message signals are extracted using the SIC, followed by the calculation of the BER. The BER is a function of signal power and fading through the channel. The scheme of SIC is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe capacity achieved by SIC for 2 users in PD-NOMA is depends on power assigned and the wireless channel. For far user the capacity R\u003csub\u003ef\u003c/sub\u003e is given by [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({R}_{f}={log}_{2}(1+\\frac{{\\left|{h}_{f}\\right|}^{2}P{\\alpha }_{f}}{{\\left|{h}_{f}\\right|}^{2}P{\\alpha }_{n}+{\\sigma }^{2}}\\)\u003c/span\u003e \u003c/span\u003e) (4)\u003c/p\u003e \u003cp\u003eWhere P is the total power. Normally higher power (P. α\u003csub\u003ef\u003c/sub\u003e ) assigned to far user compensate the attenuation of the wireless channel. For near user the capacity R\u003csub\u003en\u003c/sub\u003e is given by\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({R}_{n}={log}_{2}(1+\\frac{{\\left|{h}_{n}\\right|}^{2}P{\\alpha }_{n}}{{\\sigma }^{2}}\\)\u003c/span\u003e \u003c/span\u003e ) (5)\u003c/p\u003e \u003cp\u003eWhere, α\u003csub\u003ef\u003c/sub\u003e and α\u003csub\u003en\u003c/sub\u003e are the power allocation coefficient for far and near user respectively. The sum of these coefficients is 1. Hence the optimum power allocation is essential for faithful communication. In this paper, PD-UFMC with optimum power allocation is simulated for performance analysis.\u003c/p\u003e"},{"header":"2. Universal Filtered Multi-Carrier (UFMC)","content":"\u003cp\u003eSeveral multicarrier techniques are emerging as strong candidates for 5G waveforms, aiming to improve spectrum efficiency beyond what OFDM can achieve. The primary design objective for 5G waveforms is their flexibility and scalability to cater to various applications across diverse and heterogeneous domains. A thorough review and analysis of major waveform contenders for 5G, including Generalized Frequency Division Multiplexing (GFDM), Filter Bank Multicarrier (FBMC), Filtered-OFDM (F-OFDM), and Universal Filtered Multi-Carrier (UFMC), have been conducted. GFDM and FBMC, leveraging subcarrier filtering, offer robustness against Inter-Symbol Interference (ISI). However, they require a completely new transceiver design, lacking backward compatibility with 4G-LTE [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. To address these limitations, researchers have explored alternative modulation techniques, favouring sub-band filtering over subcarrier-wise filtering for flexibility. UFMC, employing sub-band filtering where each sub-band covers consecutive subcarriers, emerges as a promising technique.\u003c/p\u003e \u003cp\u003eUFMC combines the strengths of both OFDM and single-carrier modulation. At the transmitter, a bank of sub-filters shapes the spectrum and mitigates interference, achieving greater spectral efficiency without a cyclic prefix (CP) and reducing out-of-band emissions. Additionally, UFMC demonstrates resilience against frequency-selective fading channels, making it suitable for challenging channel conditions. The mathematical model governing UFMC encompasses signal representation, sub-filter design, subcarrier modulation, sub-filtering, channel modelling, receiver processing, subcarrier demodulation, equalization, and data decoding [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA UFMC system with N subcarriers and L sub-filters is examined. The baseband input signal, x(t), is band-limited with a bandwidth of B and sampled at a rate of fs\u0026thinsp;=\u0026thinsp;N/T, where T is the symbol duration. L sub-filters shape the spectral response of the transmitted signal, with each sub-filter designed to overlap frequency responses for desired shaping. Subcarriers, modulated by complex-valued symbols from a modulation alphabet, are equally spaced in frequency. After modulation, subcarriers are convolved with corresponding sub-filters, and their outputs are summed to yield the UFMC signal, z(t). At the receiver, the received signal undergoes matched filtering matched to transmitter sub-filters to compensate for inter symbol interference (ISI). The demodulated symbols are then equalized to mitigate channel effects and decoded to recover transmitted data [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e"},{"header":"3. Power Domain UFMC","content":"\u003cp\u003ePower Domain Non-Orthogonal Multiple Access (PD-NOMA) emerges as a promising Radio Access Technique (RAT) for the forthcoming generation of wireless communication systems. It facilitates the support of multiple users by leveraging frequency domain overloading, while assigning distinct power allocations from the overall available transmit power pool. Through the formation of user groups exhibiting significant Signal to Interference Noise Ratio (SINR) disparities, PD-NOMA effectively boosts spectral efficiency. To further optimize this system, Universal Filtered Multi-Carrier (UFMC), a versatile OFDM waveform, proves advantageous. UFMC not only offers flexibility but also mitigates the necessity for stringent synchronization requirements [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExpanding upon UFMC, Power Domain UFMC (PD-UFMC) is introduced, wherein subcarriers are organized into multiple power domains, with each domain assigned a specific power level. This configuration enables more efficient power allocation, interference management, and overall system performance optimization. PD-UFMC possesses the capability to dynamically allocate power to different domains based on channel conditions, user requirements, and quality-of-service (QoS) constraints, thereby enhancing adaptability and resource utilization [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe power spectral density PD-NOMA system with K users sharing the same time-frequency resource block. The users are indexed from k\u0026thinsp;=\u0026thinsp;1 to K. The available power is divided into J power domains, denoted as P1, P2, ..., PJ, where J\u0026thinsp;\u0026le;\u0026thinsp;K. Each power domain represents a portion of the total available power.\u003c/p\u003e \u003cp\u003eThe channel between the transmitter and each user is represented by h\u003csub\u003ek\u003c/sub\u003e, which is the channel coefficient for user k. The channel coefficients can be complex-valued, representing both magnitude and phase of the channel gain. The power allocation in PD-NOMA involves assigning power levels to each user within the available power domains. Let pk represent the power allocated to user k, where pk\u0026isin; {P1, P2, ..., PJ}. The power allocation is subject to power constraints and user-specific quality-of-service (QoS) requirements. The power spectrum for two users is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe receiver plays a crucial role in PD-NOMA systems by executing detection and decoding tasks to recover transmitted symbols from all users. Several detection techniques, including successive interference cancellation (SIC), maximum likelihood (ML) detection, or minimum mean square error (MMSE) detection, can be employed. Decoding involves making decisions based on the detected information regarding the received symbols.\u003c/p\u003e \u003cp\u003eIn PD-NOMA, effective interference management is essential for successful signal decoding from different users. Techniques such as SIC are utilized to mitigate interference from signals detected from users with higher power levels, thereby aiding in detecting signals from users with lower power levels. Both power allocation and decoding processes must consider the Quality of Service (QoS) requirements of users. QoS metrics, such as signal-to-interference-plus-noise ratio (SINR), error rate, and throughput, play a vital role in evaluating the service quality delivered to each user. The power allocation scheme should aim to optimize overall system throughput while fulfilling the QoS needs of individual users. Efficient power allocation is critical to unleashing the full potential of PD-NOMA and maximizing system throughput. This paper tackles the power optimization challenge in PD-NOMA systems by leveraging Genetic Algorithm (GA) and Moth Flame Optimization (MFO) algorithm.\u003c/p\u003e"},{"header":"4. Proposed scheme for power optimization for PD-NOMA using GA and MFO","content":"\u003cp\u003eThis section presents the power optimization framework for PD-NOMA using the GA and MFO algorithm. A mathematical model for the objective function in power optimization for power-domain non-orthogonal multiple access (PDNOMA), we need to consider the specific optimization goals and constraints of the PDNOMA system. The objective function typically aims to minimize BER.\u003c/p\u003e \u003cp\u003eN: Total number of users in the PDNOMA system\u003c/p\u003e \u003cp\u003ePe\u003csub\u003ei\u003c/sub\u003e: BER of user i, where i\u0026thinsp;=\u0026thinsp;1, 2, ...., N\u003c/p\u003e \u003cp\u003eh\u003csub\u003ei\u003c/sub\u003e: Channel gain of user i\u003c/p\u003e \u003cp\u003ew\u003csub\u003ei\u003c/sub\u003e: Weighting factor for user i.\u003c/p\u003e \u003cp\u003eNow, it can formulate the objective function for power optimization in PD-NOMA as follows:\u003c/p\u003e \u003cp\u003eMinimize: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum _{i=1}^{N}{w}_{i}*{Pe}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e4.1 GA based Optimization\u003c/h2\u003e \u003cp\u003eGenetic Algorithms (GAs) represent a category of evolutionary algorithms inspired by natural selection processes. They find extensive application in optimization scenarios characterized by large and intricate search spaces. GAs operate on a population of potential solutions, employing genetic operators such as selection, crossover, and mutation to iteratively generate new solution generations [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Optimizing Power Domain Non-Orthogonal Multiple Access (PD-NOMA) through Genetic Algorithms (GA) stands as a crucial endeavor for future wireless communication systems. PD-NOMA facilitates multiple users to share the same time-frequency resources within a wireless communication setup by allocating them to distinct power domains. Given the complexity of optimization tasks, particularly in power allocation for PD-NOMA, GA algorithms prove adept at tackling such challenges. The paper addresses a specific instance involving two users, which can be extrapolated to encompass multi-user scenarios as well.4.2 Moth Flame Optimization (MFO) based Optimization\u003c/p\u003e \u003cp\u003eThe Moth Flame Optimization (MFO) algorithm emerges as a metaheuristic optimization approach tailored for power allocation tasks within PD-NOMA systems. The paper elucidates the fundamental principles and procedural steps of the MFO algorithm, encompassing initialization, reproduction, attraction, and exploration phases. Leveraging the behavior of moths in search of optimal solutions, MFO is adept at addressing intricate optimization challenges, notably power allocation in PD-NOMA [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In contrast to Genetic Algorithms (GA), MFO demonstrates superior convergence rates. Consequently, the paper provides a comparative analysis between the two methodologies, shedding light on their respective efficacies in the context of PD-NOMA power allocation.5.0 Performance Evaluation and Comparisons\u003c/p\u003e \u003cp\u003eThe simulation environment has been developed to evaluate the BER performance of the PD-NOMA system. The simulation parameters for evaluating the power optimization by GA and MFO are given in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSimulation Parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\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\u003eModulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQAM16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChannel Coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConvolution code, rate\u0026thinsp;=\u0026thinsp;1/2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFT Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e512\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubband Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of subband\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFilter type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDolph-Chebyshev window\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFilter length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eside-lobe attenuation, dB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of Users\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNR range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 to 35 dB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWireless Channel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRayleigh Channel\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethod for interference c cancellation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSIC\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\u003eThe performance analysis has been performed by GA and MFO separately the results of optimization and their comparison are given ahead.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGA based optimization\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eThe performance of the proposed GA-based power optimization technique for PD-NOMA is evaluated through simulations. The parameters of GA are as follows,\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eNo. of Generation: 25\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePopulation Size: 15\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eElite count :2\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe convergence of the optimization has been achieved within 12 iterations. The conversion curve showing the best fitness value is given in the Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe normalized power spectrum of near and far user with GA based optimization is shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe bit error rate for different SNR value for 16-QAM has been evaluated and shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. The performance of the near user and far user are optimized with equal weight. w1 and w2 are considered 0.5 for optimization.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe performance of the near user is better than the far user which is obvious in case of the large difference between the distance of far user from base station as compared to the near user.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMFO base optimization\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eThe performance of the proposed MFO-based power optimization technique for PD-NOMA is evaluated through simulations. The parameters of MFO are as follows,\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eNo. of Iteration: 40\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePopulation Size: 15\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eConstant for defining the shape of the logarithmic spiral (b) :1\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Convergence Curve showing the stable fitness value after iteration number 24 and considered the best optimum value of the normalized power for near and far user. The power spectrum of the users with this optimization is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSimilar to the GA based optimization, the bit error rate for different SNR value for 16-QAM has been evaluated and shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e10\u003c/span\u003e. In this case also, w1 and w2 are considered 0.5 for optimization.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe paper compares the results with power allocation schemes using GA and MFO based power allocation. The performance metrics include system BER, and power efficiency. The simulation results demonstrate the superiority of the MFO-based approach in achieving higher throughput, improved fairness among users, and efficient power utilization.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparative result of GA and MFO based optimization\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOptimization with GA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOptimization with MFO\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePower to NEAR user\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePower to FAR user\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFitness Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of iteration for convergence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNR at BER 0.01 for near user\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 dB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 dB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNR at BER 0.01 for FAR user\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 dB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 dB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003ePerformance evaluations of UFMC for 5G are conducted through simulations and comparisons with other modulation techniques. The evaluations focus on key performance metrics such as spectral efficiency, error rates, and robustness to interference. The results demonstrate the superior performance of UFMC in various scenarios, including high-mobility environments, dense networks, and multi-user scenarios. Comparative studies with OFDM and other modulation schemes highlight the advantages of UFMC in terms of spectral efficiency, interference resilience, and overall system capacity. To assess the performance of PD-UFMC, extensive simulations and comparisons with existing modulation schemes are conducted. The evaluations focus on key performance metrics such as spectral efficiency, error rates, and energy efficiency. The results demonstrate the superiority of PD-UFMC over conventional OFDM and UFMC in scenarios with various channel conditions and interference levels. Furthermore, comparisons with other advanced modulation schemes, such as non-orthogonal multiple access (NOMA)\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll co-authors reviewed the manuscript and provide valuable inputs for implemetation of simulation codes .\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript .\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNarayanan, A., Rochman, M. I., Hassan, A., Firmansyah, B. S., Sathya, V., Ghosh, M., Qian, F., \u0026amp; Zhang, Z. L. (2022). May. A comparative measurement study of commercial 5G mmwave deployments. In Proceedings of the IEEE INFOCOM 2022-IEEE Conference on Computer Communications, London, UK, 2\u0026ndash;5 ; pp. 800\u0026ndash;809.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShah, F. M. S., Qasim, A. N., Karabulut, M. 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Optimal Power Flow using the Moth Flam Optimizer: A case study of the Algerian power system. \u003cem\u003eIndonesian Journal of Electrical Engineering and Computer Science\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(3), 431\u0026ndash;445.\u003c/span\u003e\u003c/li\u003e\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, PD-NOMA, UFMC, GA, MFO","lastPublishedDoi":"10.21203/rs.3.rs-4357764/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4357764/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the rise of Fifth generation (5G) communication systems, there's a growing demand for efficient multiple access techniques capable of handling high data rates and accommodating a large user base. Power Domain Non-Orthogonal Multiple Access (PD-NOMA) combined with Universal Filtered Multi-Carrier (UFMC) has emerged as a promising solution to address these needs. This combination aims to enhance spectral efficiency, capacity, and user fairness in 5G networks by capitalizing on the strengths of both PD-NOMA and UFMC. Nevertheless, the efficient allocation of power among users poses an optimization challenge within the confines of a specified power budget to achieve optimal performance in PD-NOMA-UFMC systems. This paper presents a power optimization methodology for PD-NOMA-UFMC utilizing intelligent optimization techniques, namely Genetic Algorithm (GA) and Moth Flame Optimization (MFO) algorithm. The proposed approach aims to maximize the overall system throughput by dynamically assigning power levels to users based on their individual channel conditions. Simulation results showcase the effectiveness of the power optimization technique employing GA and MFO in enhancing the performance of PD-NOMA-UFMC systems in terms of throughput, fairness, and power efficiency. Additionally, the paper discusses comparative results of optimization using GA and MFO.\u003c/p\u003e","manuscriptTitle":"Performance Enhancement through Optimal Power Allocation for Downlink PD-NOMA-UFMC in 5G Networks using Genetic algorithm and MFO","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-21 19:02:43","doi":"10.21203/rs.3.rs-4357764/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":"477e8f76-faae-49c3-9b86-bc9fe87659e8","owner":[],"postedDate":"May 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-01T01:38:20+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-21 19:02:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4357764","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4357764","identity":"rs-4357764","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}