Maximizing Deep Learning-Based Energy Efficiency in 5G Downlink MIMO-NOMA Systems by Using MLP-CNN
Keywords:
Energy Efficiency, 5G, MIMO-NOMA, Deep Learning, MLP-CNNAbstract
Multiple input multiple outputs-Nonorthogonal multiple access (MIMO-NOMA), presenting a potential technology to improve system performance and energy efficiency. Nevertheless, the system's effectiveness is hampered by the impact of swiftly changing channel conditions and intricate spatial structures, restricting its broader applicability. Deep learning plays a crucial role by helping MIMO-NOMA overcome challenges, improve Energy efficiency, and increase capacity and overall system performance in wireless communication networks. This research paper proposes a deep learning-based Multilayer Perceptron-Convolution neural network (MLP-CNN) framework. The framework optimizes the data rate and energy efficiency by addressing the power allocation problems. It can be utilized with multiple convolutional and hidden layers, trained using specific algorithms to solve power allocation problems. Simulation results demonstrate that the proposed framework improves power allocation, overall data rates, and Energy efficiency by around 15% compared to traditional deep neural network (DNN) algorithms, methods and strategies.
Downloads
References
S. P. Kumaresan, C. K. Tan and Y. H. Ng, "Efficient User Clustering Using a Low-Complexity Artificial Neural Network (ANN) for 5G NOMA Systems," IEEE Access, vol. 8, pp. 179307-179316, September 2020.
M. K. Hasan, M. Shahjalal, M. M. Islam, M. M. Alam, M. F. Ahmed, and Y. M. Jang, “The Role of Deep Learning in NOMA for 5G and Beyond Communications,” International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 303-307, February 2020.
M. Katwe, K. Singh, P. K. Sharma, C.-P. Li, and Z. Ding, “Dynamic User Clustering and Optimal Power Allocation in UAV-Assisted Full-Duplex Hybrid NOMA System,” IEEE Transactions on Wireless Communications, vol. 21, no. 4, pp. 2573-2590, September 2021. https://doi.org/10.1109/twc.2021.3113640
H. Zhang, H. Zhang, W. Liu, K. Long, J. Dong, and V. C. Leung, “Energy Efficient User Clustering, Hybrid Precoding and Power Optimization in Terahertz MIMO-NOMA Systems,” IEEE Journal on selected areas in communications, vol. 38, no. 9, pp. 2074-2085, June 2020. https://doi.org/10.1109/jsac.2020.3000888.
G. Li, M. Zeng, D. Mishra, L. Hao, Z. Ma, and O. A. Dobre, “Energy-Efficient Design for IRS-Empowered Uplink MIMO-NOMA Systems,” IEEE Transactions on Vehicular Technology, vol. 71, no. 9, pp. 9490-9500, May 2022.
Y. Song, J. Tang, C. Lin, W. Feng, Z. Chen, D. K. C. So, and K.-K. Wong, “Energy Efficiency Optimization for PSOAM Mode-Groups Based MIMO-NOMA Systems,” IEEE Transactions on Communications, vol. 70, no. 8, pp. 5679-5692, June 2022.
N. Prabakaran and R. P. Devi, “An Improved Deep Learning Framework for Enhancing MIMO-NOMA System Performance,” Multimedia Tools and Applications, vol. 82, pp. 1-28, August 2023.
Sk, Khaleelahmed, and VenkateswaraRao N., “An Approach for Energy-Efficient Power Allocation in MIMO-NOMA System,” International Journal of Electronics, vol. 109, no. 6, pp. 953-970, June 2022.
A. Bhardwaj, R. Raman, J. Singh, K. Pant, N. Yamsani, and R. Yadav, “Deep Learning-Based MIMO and NOMA Energy Conservation and Sum Data Rate Management System,” 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), pp. 866-871, May 2023.
M. Ravi, T. A. Sheikh, and Y. Bulo, “Improve Energy Efficiency and Minimise BER in MIMO-NOMA System With Max-Min Power Control Algorithm,” International Journal of Systems, Control and Communications, vol. 14, no. 3, pp. 254-273, November 2023.
S. Rajak, P. Selvaprabhu, S. Chinnadurai, A. S. Hosen, A. Saad, and A. Tolba, “Energy Efficient MIMO-NOMA Aided IoT Network in B5G Communications,” Computer Networks, vol. 216, p. 109250, October 2022.
A. Jawarneh, M. Kadoch, and Z. Albataineh, “Decoupling Energy Efficient Approach for Hybrid Precoding-Based mmWave Massive MIMO-NOMA With SWIPT,” IEEE Access, vol. 10, pp. 28868-28884, February 2022.
V. Andiappan and V. Ponnusamy, “Deep Learning Enhanced NOMA System: A Survey on Future Scope and Challenges,” Wireless Personal Communications, vol. 123, no. 1, pp. 1-39, March 2022.
R. Zhang, K. Xiong, Y. Lu, D. W. K. Ng, P. Fan, and K. B. Letaief, “SWIPT-Enabled Cell-Free Massive MIMO-NOMA Networks: A Machine Learning-Based Approach,” IEEE Transactions on Wireless Communications, vol. 18, pp. 1 – 1, November 2023.
S. Mirbolouk, M. Valizadeh, M. C. Amirani, and S. Ali, “Relay Selection and Power Allocation for Energy Efficiency Maximization in Hybrid Satellite-UAV Networks With CoMP-NOMA Transmission,” IEEE Transactions on Vehicular Technology, vol. 71, no. 5, pp. 5087-5100, February 2022.
A. B. Adam, Z. Wang, X. Wan, Y. Xu, and B. Duo, “Energy-Efficient Power Allocation in Downlink Multi-Cell Multi-Carrier NOMA: Special Deep Neural Network Framework,” IEEE Transactions on Cognitive Communications and Networking, vol. 8, no. 4, pp. 1770-1783, August 2022.
S. Sabapathy, A. Krishnan, N. Nedoumarane, S. Maruthu, and D. N. K. Jayakody, “Deep Learning Based Simultaneous Wireless Information and Power Transfer Enabled Massive MIMO-NOMA for Beyond 5G,” International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), pp. 1-6, May 2023.
M. K. Beuria and S. S. Singh, “Performance Analysis of Cooperative NOMA With Optimized Power Allocation Using Deep Learning Approach,” Wireless Networks, vol. 29, pp. 1-16, October 2023.
R. Aldebes, K. Dimyati, and E. Hanafi, “Genetic Algorithm for Optimizing Energy Efficiency in Downlink mmWave NOMA System With Imperfect CSI,” Symmetry, vol. 14, no. 11, p. 2345, November 2022.
K. R. Chandra and S. Borugadda, “Multi Agent Deep Reinforcement Learning With Deep Q-Network Based Energy Efficiency and Resource Allocation in NOMA Wireless Systems,” 2nd International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT), pp. 1-8, April 2023.
A. Kumar and K. Kumar, “Deep Learning-Based Joint NOMA Signal Detection and Power Allocation in Cognitive Radio Networks,” IEEE Transactions on Cognitive Communications and Networking, vol. 8, no. 4, pp. 1743-1752, July 2022.
S. Norouzi, B. Champagne, and Y. Cai, “Joint Optimization Framework for User Clustering, Downlink Beamforming, and Power Allocation in MIMO-NOMA Systems,” IEEE Transactions on Communications, vol. 71, no. 1, pp. 214-228, November 2022. https://doi.org/10.1109/tcomm.2022.3222374.
Z. Zhang, M. Hua, C. Li, Y. Huang, and L. Yang, “Beyond Supervised Power Control in Massive MIMO Network: Simple Deep Neural Network Solutions,” IEEE Transactions on Vehicular Technology, vol. 71, no. 4, pp. 3964-3979, January 2022.
H. Huang, Y. Yang, Z. Ding, H. Wang, H. Sari, and F. Adachi, “Deep Learning-Based Sum Data Rate and Energy Efficiency Optimization for MIMO-NOMA Systems,” IEEE Transactions on Wireless Communications, vol. 19, no. 8, pp. 5373-5388, May 2020.
X. Wei, H. Al-Obiedollah, K. Cumanan, Z. Ding and O. A. Dobre, “Energy Efficiency Maximization for Hybrid TDMA-NOMA System With Opportunistic Time Assignment,” IEEE Transactions on Vehicular Technology, vol. 71, no. 8, pp. 8561-8573, August 2022.
H. Han, X. Jiang, W. Lu, W. Zhai, Y. Li, N. Kumar, and M. Guizani, “A Multi-Agent Reinforcement Learning Approach for Massive Access in NOMA-URLLC Networks,” IEEE Transactions on Vehicular Technology, vol. 10, pp. 1-7, July 2023.doi: https://doi.org/10.1109/tvt.2023.3292423.
W. Lee, H. Lee, and H.-H. Choi, “Deep Learning-Based Network-Wide Energy Efficiency Optimization in Ultra-Dense Small Cell Networks,” IEEE Transactions on Vehicular Technology, vol. 72, pp. 8244 - 8249, January 2023.
A. J. Muhammed, H. Chen, A. M. Seid, Z. Han, and Q. Yu, “Energy-Efficient Resource Allocation for NOMA HetNets in Millimeter Wave Communications,” IEEE Transactions on Wireless Communications, vol. 22, pp. 3790 - 3804, November 2022.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
