A Machine Learning-Based Framework for Resource Allocation, Energy Efficiency, and Network Performance
Keywords:
Artificial Intelligence, Machine Learning, 5G, 6G, Deep Reinforcement Learning, Resource AllocationAbstract
With the deployment of the fifth-generation (5G) wireless communication networks, to the next-generation sixth-generation (6G) networks, the network have been presented with newer and higher demand for network capacity, spectrum efficiency, energy efficiency, latency, reliability and quality of service (QoS). Standard optimization techniques might have fixed solutions and allocation policies, which makes them less adaptable to the fast-changing traffic volumes and channel conditions. Artificial intelligence (AI) and machine learning (ML) provide the promise of helping to manage a network in an adaptive way. This paper presents a novel framework for optimization of 5G and future 6G wireless communication scenarios based on AI. The framework takes the following network parameters as the input to an intelligent optimization engine: network traffic, channel quality, user demand, bandwidth, and transmission power. Two approaches, namely, conventional resource allocation and genetic algorithm optimization, are compared against the proposed approach of deep reinforcement learning (DRL) as the main adaptive decision-making approach. Throughput, end-to-end delay, energy efficiency, PDR, packet loss, and spectral efficiency are identified as evaluation metrics. The proposed study will be correlated via simulation in varying traffic and channel conditions. At this stage no numerical improvement in performance is claimed and this needs to be confirmed by repeatable experiments, which will provide quantitative results.
References
[1] J. G. Andrews et al., “What will 5G be?,” IEEE Journal on Selected Areas in Communications, vol. 32, no. 6, pp. 1065-1082, 2014.
[2] K. B. Letaief, W. Chen, Y. Shi, J. Zhang, and Y.-J. A. Zhang, “The roadmap to 6G: AI empowered wireless networks,” IEEE Communications Magazine, vol. 57, no. 8, pp. 84-90, 2019.
[3] W. Saad, M. Bennis, and M. Chen, “A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,” IEEE Network, vol. 34, no. 3, pp. 134-142, 2020.
[4] P. Yang, Y. Xiao, M. Xiao, and S. Li, “6G wireless communications: Vision and potential techniques,” IEEE Network, vol. 33, no. 4, pp. 70-75, 2019.
[5] M. Chen, U. Challita, W. Saad, C. Yin, and M. Debbah, “Artificial neural networks-based machine learning for wireless networks: A tutorial,” IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3039-3071, 2019.
[6] C. Zhang, P. Patras, and H. Haddadi, “Deep learning in mobile and wireless networking: A survey,” IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2224-2287, 2019.
[7] R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, 2nd ed. MIT Press, 2018.
[8] V. Mnih et al., “Human-level control through deep reinforcement learning,” Nature, vol. 518, no. 7540, pp. 529-533, 2015.
[9] N. C. Luong et al., “Applications of deep reinforcement learning in communications and networking: A survey,” IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3133-3174, 2019.
[10] A. Alwarafy, M. Abdallah, B. S. Ciftler, A. Al-Fuqaha, and M. Hamdi, “Deep reinforcement learning for radio resource allocation and management in next generation heterogeneous wireless networks: A survey,” arXiv preprint arXiv:2106.00574, 2021.
[11] H. Ye, G. Y. Li, and B.-H. F. Juang, “Deep reinforcement learning based resource allocation for V2V communications,” IEEE Transactions on Vehicular Technology, vol. 68, no. 4, pp. 3163-3173, 2019.
[12] Y. S. Nasir and D. Guo, “Multi-agent deep reinforcement learning for dynamic power allocation in wireless networks,” IEEE Journal on Selected Areas in Communications, vol. 37, no. 10, pp. 2239-2250, 2019.
[13] Q. Wu, G. Y. Li, W. Chen, D. W. K. Ng, and R. Schober, “An overview of sustainable green 5G networks,” IEEE Wireless Communications, vol. 24, no. 4, pp. 72-80, 2017.
[14] M. Usama and M. Erol-Kantarci, “A survey on recent trends and open issues in energy efficiency of 5G,” Sensors, vol. 19, no. 14, Art. no. 3126, 2019.
[15] D. López-Pérez et al., “A survey on 5G radio access network energy efficiency: Massive MIMO, lean carrier design, sleep modes, and machine learning,” IEEE Communications Surveys & Tutorials, vol. 24, no. 1, pp. 653-697, 2022.
[16] T. P. Fowdur and B. Doorgakant, “A review of machine learning techniques for enhanced energy efficient 5G and 6G communications,” Engineering Applications of Artificial Intelligence, vol. 122, Art. no. 106032, 2023.
[17] A. Ali, G. A. Shah, and J. Arshad, “Energy efficient resource allocation for M2M devices in 5G,” Sensors, vol. 19, no. 8, Art. no. 1830, 2019.
[18] X. Yao and A. Pérez Yuste, “A ML-based resource allocation scheme for energy optimization in 5G NR,” Sensors, vol. 25, no. 16, Art. no. 4978, 2025.
[19] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182-197, 2002.
