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Optimizing Wireless Networks with Deep Unfolding: Comparative Study on Two Deep Unfolding Mechanisms
Published 17 Feb 2024 in cs.NI and cs.LG | (2403.18930v1)
Abstract: In this work, we conduct a comparative study on two deep unfolding mechanisms to efficiently perform power control in the next generation wireless networks. The power control problem is formulated as energy efficiency over multiple interference links. The problem is nonconvex. We employ fractional programming transformation to design two solutions for the problem. The first solution is a numerical solution while the second solution is a closed-form solution. Based on the first solution, we design a semi-unfolding deep learning model where we combine the domain knowledge of the wireless communications and the recent advances in the data-driven deep learning. Moreover, on the highlights of the closed-form solution, fully deep unfolded deep learning model is designed in which we fully leveraged the expressive closed-form power control solution and deep learning advances. In the simulation results, we compare the performance of the proposed deep learning models and the iterative solutions in terms of accuracy and inference speed to show their suitability for the real-time application in next generation networks.
- W. Jiang, B. Han, M. A. Habibi, and H. D. Schotten, “The Road Towards 6G: A Comprehensive Survey,” IEEE open j. Commun. Soc., vol. 2, pp. 334-366, 2021.
- R. Li, “Network 2030 A Blueprint of Technology, Applications and Market Drivers Towards the Year 2030 and Beyond,” July 2020.
- 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.
- M. A. Ouamri, G. Barb, D. Singh, A. B. M. Adam, M. S. A. Muthanna and X. Li, “Nonlinear Energy-Harvesting for D2D Networks Underlaying UAV With SWIPT Using MADQN,” IEEE Commun. Lett., vol. 27, no. 7, pp. 1804-1808, July 2023.
- A. B. M. Adam, X. Wan and Z. Wang, “Energy Efficiency Maximization in Downlink Multi-Cell Multi-Carrier NOMA Networks With Hardware Impairments,” IEEE Access, vol. 8, pp. 210054-210065, 2020.
- A. B. M. Adam, X. Wan, Z. Wang, “Energy Efficiency Maximization for Multi-Cell Multi-Carrier NOMA Networks,” Sensors, 2020, 20(22):6642.
- A. B. M. Adam, X. Wan, Z. Wang, “Clustering and Auction-Based Power Allocation Algorithm for Energy Efficiency Maximization in Multi-Cell Multi-Carrier NOMA Networks,” App. Sci., 2019; 9(23):5034.
- A. B. M. Adam, X. Wan, Z. Wang, “User scheduling and power allocation for downlink multi-cell multi-carrier NOMA systems,” Digital Communications and Networks, vol. 9, no. 1, pp. 252-263, 2023.
- B. Marinberg, A. Cohen, E. Ben-Dror, and H. H. Permuter, “A Study on MIMO Channel Estimation by 2D and 3D Convolutional Neural Networks,” in Proc. IEEE Int. Conf. Adv. Netw. Telecommun. Sys. (ANTS), 2020, pp. 1-6.
- H. Ye, G. Y. Li, and B. H. Juang, “Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems,” IEEE Wireless Commun. Lett., vol. 7, no. 1, pp. 114-117, 2018.
- H. Huang, J. Yang, H. Huang, Y. Song, and G. Gui, “Deep Learning for Super-Resolution Channel Estimation and DOA Estimation Based Massive MIMO System,” IEEE Trans. Veh. Technol., vol. 67, no. 9, pp. 8549-8560, 2018.
- Z. Mao and S. Yan, “Deep learning based channel estimation in fog radio access networks,” China Communications, vol. 16, no. 11, pp. 16-28, 2019.
- Q. Bai, J. Wang, Y. Zhang, and J. Song, “Deep Learning-Based Channel Estimation Algorithm Over Time Selective Fading Channels,” IEEE Trans. Cogn. Commun. Netw., vol. 6, no. 1, pp. 125-134, 2020.
- L. Wan, K. Liu, and W. Zhang, “Deep Learning-Aided Off-Grid Channel Estimation for Millimeter Wave Cellular Systems,” IEEE Trans. Wireless Commun., pp. 1-1, 2021.
- A. B. M. Adam and X. Wan, “Deep Learning based Efficient User Association and Subchannel Assignment in Multi-Cell Multi-Carrier NOMA Networks,” in Proc. 7th Int. Conf. Inf. Sci. Control Eng. (ICISCE), Changsha, China, Dec. 2020, pp. 448-452.
- M. Labana and W. Hamouda, “Unsupervised Deep Learning Approach for Near Optimal Power Allocation in CRAN,” IEEE Trans. Veh. Technol., vol. 70, no. 7, pp. 7059-7070, 2021.
- A. B. M. 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 Tran. Cogn. Commun. Netw., vol. 8, no. 4, pp. 1770-1783, Dec. 2022.
- A. B. M. Adam, L. Lei, S. Chatzinotas, and N. U. R. Junejo, “Deep Convolutional Self-Attention Network for Energy-Efficient Power Control in NOMA Networks,” IEEE Trans. Veh. Technol., vol. 71, no. 5, pp. 5540-5545, May 2022.
- A. Jagannath, J. Jagannath, and T. Melodia, “Redefining Wireless Communication for 6G: Signal Processing Meets Deep Learning With Deep Unfolding,” IEEE Trans. Artificial Intell., vol. 2, no. 6, pp. 528-536, 2021.
- D. Ito, S. Takabe, and T. Wadayama, “Trainable ISTA for Sparse Signal Recovery,”IEEE Trans. Signal Process., vol. 67, no. 12, pp. 3113-3125, 2019.
- S. Khobahi and M. Soltanalian, “Model-Based Deep Learning for One-Bit Compressive Sensing,” IEEE Trans. Signal Process., vol. 68, pp. 5292-5307, 2020.
- Q. Hu, Y. Cai, Q. Shi, K. Xu, G. Yu, and Z. Ding, “Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems,” IEEE Trans. Wireless Commun., vol. 20, no. 2, pp. 1394-1410, 2021.
- G. Zhang, X. Fu, Q. Hu, Y. Cai, and G. Yu, “Hybrid Precoding Design Based on Dual-Layer Deep-Unfolding Neural Network,” in Proc. IEEE 32nd Annual Int. Symp. Personal, Indoor Mobile Radio Commun. (PIMRC), 2021, pp. 678-683.
- C. H. Lin, Y. T. Lee, W. H. Chung, S. C. Lin, and T. S. Lee, “Unsupervised ResNet-Inspired Beamforming Design Using Deep Unfolding Technique,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), 2020, pp. 1-7.
- S. Takabe and T. Wadayama, “Deep Unfolded Multicast Beamforming,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), 2020, pp. 1-6.
- Y. Liu, Q. Hu, Y. Cai, G. Yu, and G. Y. Li, “Deep-Unfolding Beamforming for Intelligent Reflecting Surface assisted Full-Duplex Systems,” IEEE Trans. Wireless Commun., pp. 1-1, 2021.
- A. Chowdhury, G. Verma, C. Rao, A. Swami, and S. Segarra, “Unfolding WMMSE Using Graph Neural Networks for Efficient Power Allocation,” IEEE Trans. Wireless Commun., vol. 20, no. 9, pp. 6004-6017, 2021.
- K. Shen and W. Yu, “Fractional Programming for Communication Systems-Part I: Power Control and Beamforming,” IEEE Trans. Signal Process., vol. 66, no. 10, pp. 2616-2630, 2018.
- A. Zappone, L. Sanguinetti, G. Bacci, E. Jorswieck, and M. Debbah, “Energy-Efficient Power Control: A Look at 5G Wireless Technologies,” IEEE Trans. Signal Process., vol. 64, no. 7, pp. 1668-1683, 2016.
- M. Grant and S. Boyd, “CVX: Matlab Software for Disciplined Convex Programming, Version 3.0 Beta,” Available online: http://cvxr.com/cvx/beta/, 2017.
- K. Shen and W. Yu, “Fractional Programming for Communication Systems-Part II: Uplink Scheduling via Matching,” IEEE Trans. Signal Process., vol. 66, no. 10, pp. 2631-2644, 2018.
- K. -L. Besser, B. Matthiesen, A. Zappone and E. A. Jorswieck, “Deep Learning Based Resource Allocation: How Much Training Data is Needed?,” 2020 In Proc. IEEE 21st Int. Workshop Signal Process. Advances Wireless Commun. (SPAWC), Atlanta, GA, USA, 2020, pp. 1-5.
- Y. Liu, Q. Hu, Y. Cai, G. Yu and G. Y. Li, “Deep-Unfolding Beamforming for Intelligent Reflecting Surface assisted Full-Duplex Systems,” IEEE Trans. Wireless Commun., vol. 21, no. 7, pp. 4784-4800, July 2022.
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