Call for Paper - Upcoming Issues

Ensuring QoS for NOMA Using GA Based Power Allocation Scheme with a Variant of Greedy Heuristic Pairing Algorithm

International Journal of Electronics and Communication Engineering
© 2025 by SSRG - IJECE Journal
Volume 12 Issue 7
Year of Publication : 2025
Authors : Najuk Parekh, Rutvij Joshi, Twinkle Doshi
10.14445/23488549/IJECE-V12I7P105
pdf
How to Cite?

Najuk Parekh, Rutvij Joshi, Twinkle Doshi, "Ensuring QoS for NOMA Using GA Based Power Allocation Scheme with a Variant of Greedy Heuristic Pairing Algorithm," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 7, pp. 47-61, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I7P105

Abstract:

Non-Orthogonal Multiple Access (NOMA) is emerging as a pivotal enabling mechanism for next-generation wireless communication systems. Due to its advanced integration capabilities, NOMA is a foundational and transformative access technology, which is key in addressing the evolving demands of upcoming network architecture. By effectively optimizing system parameters, NOMA can significantly contribute to green communication by reducing energy consumption and enhancing resource utilization efficiency. This article proposes an effective Multi-Objective Genetic Algorithm (MOGA)-based power allocation strategy integrated with a variant of the Greedy Heuristic Pairing Algorithm for Single-Carrier Non-Orthogonal Multiple Access (SC-NOMA) downlink systems, aiming to ensure user-oriented Quality of Service (QoS). To address this, the proposed scheme optimizes power allocation by exploiting MOGA, which simultaneously enhances the overall system sum rate and ensures fairness among users. User pairing is conducted through a Greedy Heuristic Algorithm, which strategically clusters users based on maximum differences in channel gains, thereby effectively exploiting the advantages of power-domain multiplexing. Extensive analytical and Monte Carlo simulation results confirm the effectiveness of the proposed methodology, demonstrating substantial improvements in outage performance and QoS guarantees for both cell-edge and cell-centred users. It is also revealed that when optimized via MOGA, three-user clustering provides better outage performance even at lower Signal-to-Noise Ratios (SNRs) compared to conventional two-user pairing schemes. This research paves the way for future exploration of advanced multi-objective evolutionary algorithms to enhance the NOMA system's efficiency and reliability.

Keywords:

5G, MOGA, Outage analysis, QoS, Decent work and economic growth.

References:

[1] “Report ITU-R M.2083-0, IMT Vision – Framework and Overall Objectives of the Future Development of IMT for 2020 and Beyond,” Recommendation International Telecommunication Union, pp. 1-21, 2015.
[Google Scholar] [Publisher Link]
[2] “Report ITU-R M.2320-0, Future Technology Trends of Terrestrial IMT Systems,” Recommendation International Telecommunication Union, pp. 1-32, 2014.
[Google Scholar] [Publisher Link]
[3] Anass Benjebbour et al., “Concept and Practical Considerations of Non-Orthogonal Multiple Access (NOMA) for Future Radio Access,” 2013 International Symposium on Intelligent Signal Processing and Communication Systems, Naha, Japan, pp. 770-774, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Heunchul Lee, Sungsoo Kim, and Jong-Han Lim, “Multiuser Superposition Transmission (MUST) for LTE-A Systems,” 2016 IEEE International Conference on Communications (ICC), Kuala Lumpur, Malaysia, pp. 1-6, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Linglong Dai et al., “Non-Orthogonal Multiple Access for 5G: Solutions, Challenges, Opportunities, and Future Research Trends,” IEEE Communications Magazine, vol. 53, no. 9, pp. 74-81, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Zain Ali et al., “Joint User Pairing, Channel Assignment, and Power Allocation in NOMA based CR Systems,” Applied Sciences, vol. 9, no. 20, pp. 1-17, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Zhiguo Ding et al., “On the Performance of Non-Orthogonal Multiple Access in 5G Systems with Randomly Deployed Users,” IEEE Signal Processing Letters, vol. 21, no. 12, pp. 1501-1505, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Zhiguo Ding, Pingzhi Fan, and H. Vincent Poor, “Impact of User Pairing on 5G Non-Orthogonal Multiple Access Downlink Transmissions,” IEEE Transactions on Vehicular Technology, vol. 65, no. 8, pp. 6010-6023, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Najuk Parekh, and Rutvij Joshi, “Non Orthogonal Multiple Access Techniques for Next Generation Wireless Networks: A Review,” Proceedings of the International e-Conference on Intelligent Systems and Signal Processing, Singapore, pp. 171-188, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Aiman Kassir et al., “Power Domain Non Orthogonal Multiple Access: A Review,” 2018 2nd International Conference on Telematics and Future Generation Networks (TAFGEN), Kuching, Malaysia, pp. 66-71, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Mohamed M. El-Sayed, Ahmed S. Ibrahim, and Mohamed M. Khairy, “Power Allocation Strategies for Non-Orthogonal Multiple Access,” 2016 International Conference on Selected Topics in Mobile & Wireless Networking (MoWNeT), Cairo, Egypt, pp. 1-6, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[12] XianTian Luo et al., “Research on Power Allocation Algorithm in Non-Orthogonal Multiple Access Systems,” 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA), Xi'an, China, pp. 1084-1089, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Zheng Yang et al., “Outage Performance for Dynamic Power Allocation in Hybrid Non-Orthogonal Multiple Access Systems,” IEEE Communications Letters, vol. 20, no. 8, pp. 1695-1698, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Fang Fang et al., “Energy-Efficient Resource Allocation for Downlink Non-Orthogonal Multiple Access Network,” IEEE Transactions on Communications, vol. 64, no. 9, pp. 3722-3732, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Sotirios K. Goudos et al., “Joint User Association and Power Allocation Using Swarm Intelligence Algorithms in Non-Orthogonal Multiple Access Networks,” 2020 9th International Conference on Modern Circuits and Systems Technologies (MOCAST), Bremen, Germany, pp. 1-4, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Priyabrata Parida, and Suvra Sekhar Das, “Power Allocation in OFDM based NOMA Systems : A DC Programming Approach,” 2014 IEEE Globecom Workshops (GC Wkshps), Austin, TX, USA, pp. 1026-1031, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Abdullah Konak, David W. Coit, and Alice E. Smith, “Multi-Objective Optimization using Genetic Algorithms: A Tutorial,” Reliability Engineering & System Safety, vol. 91, no. 9, pp. 992-1007, 2006.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Jinli Zhang et al., “The 5G NOMA Networks Planning based on the Multi-Objective Evolutionary Algorithm,” 2020 16th International Conference on Computational Intelligence and Security (CIS), Guangxi, China, pp. 59-62, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Shelesh Krishna Saraswat, Vinay Kumar Deolia, and Aasheesh Shukla, “Allocation of Power in NOMA based 6G-Enabled Internet of Things using Multi-Objective based Genetic Algorithm,” Journal of Electrical Engineering, vol. 74, no. 2, pp. 95-101, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[20] K. Deb et al., “A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182-197, 2002.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Jinho Choi et al., “Power Allocation for Max-Sum Rate and Max-Min Rate Proportional Fairness in NOMA,” IEEE Communications Letters, vol. 20, no. 10, pp. 2055-2058, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Hongyun Xiao et al., “An Improved PSO-Based Power Allocation Algorithm for the Optimal EE and SE Tradeoff in Downlink NOMA Systems,” 2018 IEEE 29th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Bologna, Italy, pp. 1-5, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Energy and Spectral Efficiency Tradeoff in NOMA: Multi-Objective Evolutionary Approaches,” 2020 IEEE International Conference on Communications Workshops (ICC Workshops), Dublin, Ireland, s2020.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Akash Agarwal et al., “Outage Probability Analysis for NOMA Downlink and Uplink Communication Systems with Generalized Fading Channels,” IEEE Access, vol. 8, pp. 220461-220481, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[25] George T. Heineman, Learning Algorithms : A Programmer’s Guide to Writing Better Code, O’Reilly Media, pp. 1-263, 2021.
[Google Scholar] [Publisher Link]