Deep Learning Based Power Allocation Schemes for NOMA System with Imperfect SIC
DOI:
https://doi.org/10.13052/jmm1550-4646.19110Keywords:
Non-orthogonal multiple access (NOMA), power allocation, deep learning, energy efficiency, imperfect SICAbstract
Non-orthogonal multiple access (NOMA) has been a promising technology for 5G communication system. NOMA allows more than one user to utilize the same resource at the same time, which can lead to high performance in terms of spectral efficiency, energy efficiency or fairness. NOMA enables power domain multiplexing and separates the multiplexed signal by using successive interference cancellation (SIC). Therefore, the full benefit of NOMA depends on power allocation. However, in practical system, residual interference caused by SIC process can severely degrade a system performance. In this paper, we propose two power allocation schemes based on deep learning to alleviate the effect of imperfect SIC for downlink NOMA system, including a deep learning-based sum rate power allocation scheme (DL-SRPAS) and a deep learning-based energy-efficient power allocation scheme (DL-EEPAS). our two proposed schemes learn two optimal power allocation schemes provided through exhaustive search. We search the solution of the optimization problems, where two optimization problems are formulated to maximize the sum rate and the energy efficiency subject to a minimum user data rate requirement. The simulation results verify that our two proposed schemes can alleviate the effect of imperfect SIC and outperform two conventional power allocation schemes which maximize the sum rate and the energy efficiency without considering imperfect SIC. In addition, our proposed schemes achieve near-optimal performance with very low computational time.
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J. G. Andrews, S. Buzzi, W. Choi, S. V. Hanly, A. Lozano, A. C. K. Soong, and J. C. Zhang. What will 5g be? IEEE Journal on Selected Areas in Communications, 32(6):1065–1082, June 2014.
J. Wang, W. Guan, Y. Huang, R. Schober, and X. You. Distributed optimization of hierarchical small cell networks: A gnep framework. IEEE Journal on Selected Areas in Communications, 35(2):249–264, Feb 2017.
J. G. Andrews, H. Claussen, M. Dohler, S. Rangan, and M. C. Reed. Femtocells: Past, present, and future. IEEE Journal on Selected Areas in Communications, 30(3):497–508, April 2012.
X. Wang, L. Kong, F. Kong, F. Qiu, M. Xia, S. Arnon, and G. Chen. Millimeter wave communication: A comprehensive survey. IEEE Communications Surveys Tutorials, 20(3):1616–1653, thirdquarter 2018.
J. Liu, N. Kato, J. Ma, and N. Kadowaki. Device-to-device communication in lte-advanced networks: A survey. IEEE Communications Surveys Tutorials, 17(4):1923–1940, Fourthquarter 2015.
D. C. Araújo, T. Maksymyuk, A. L. F. de Almeida, T. Maciel, J. C. M. Mota, and M. Jo. Massive mimo: survey and future research topics. IET Communications, 10(15):1938–1946, 2016.
Y. Saito, A. Benjebbour, Y. Kishiyama, and T. Nakamura. System-level performance evaluation of downlink non-orthogonal multiple access (noma). In 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), pages 611–615, Sep. 2013.
P. Wang, J. Xiao, and L. P. Comparison of orthogonal and non-orthogonal approaches to future wireless cellular systems. IEEE Vehicular Technology Magazine, 1(3):4–11, Sep. 2006.
Y. Saito, Y. Kishiyama, A. Benjebbour, T. Nakamura, A. Li, and K. Higuchi. Non-orthogonal multiple access (noma) for cellular future radio access. In 2013 IEEE 77th Vehicular Technology Conference (VTC Spring), pages 1–5, June 2013.
L. Dai, B. Wang, Y. Yuan, S. Han, C. I, and Z. Wang. Non-orthogonal multiple access for 5g: solutions, challenges, opportunities, and future research trends. IEEE Communications Magazine, 53(9):74–81, Sep. 2015.
S. M. R. Islam, N. Avazov, O. A. Dobre, and K. Kwak. Power-domain non-orthogonal multiple access (noma) in 5g systems: Potentials and challenges. IEEE Communications Surveys Tutorials, 19(2):721–742, Secondquarter 2017.
C. Wang, J. Chen, and Y. Chen. Power allocation for a downlink non-orthogonal multiple access system. IEEE Wireless Communications Letters, 5(5):532–535, Oct 2016.
P. Parida and S. S. Das. Power allocation in ofdm based noma systems: A dc programming approach. In 2014 IEEE Globecom Workshops (GC Wkshps), pages 1026–1031, Dec 2014.
M. Hojeij, J. Farah, C. A. Nour, and C. Douillard. Resource allocation in downlink non-orthogonal multiple access (noma) for future radio access. In 2015 IEEE 81st Vehicular Technology Conference (VTC Spring), pages 1–6, May 2015.
Z. Yang, W. Xu, C. Pan, Y. Pan, and M. Chen. On the optimality of power allocation for noma downlinks with individual qos constraints. IEEE Communications Letters, 21(7):1649–1652, July 2017.
Y. Zhang, H. Wang, T. Zheng, and Q. Yang. Energy-efficient transmission design in non-orthogonal multiple access. IEEE Transactions on Vehicular Technology, 66(3):2852–2857, March 2017.
F. Fang, H. Zhang, J. Cheng, and V. C. M. Leung. Energy-efficient resource allocation for downlink non-orthogonal multiple access network. IEEE Transactions on Communications, 64(9):3722–3732, Sep. 2016.
M. Zeng, A. Yadav, O. A. Dobre, and H. V. Poor. Energy-efficient power allocation for hybrid multiple access systems. In 2018 IEEE International Conference on Communications Workshops (ICC Workshops), pages 1–5, May 2018.
A. Benjebbour, Y. Saito, Y. Kishiyama, A. Li, A. Harada, and T. Nakamura. Concept and practical considerations of non-orthogonal multiple access (noma) for future radio access. In 2013 International Symposium on Intelligent Signal Processing and Communication Systems, pages 770–774, Nov 2013.
T. O’Shea and J. Hoydis. An introduction to deep learning for the physical layer. IEEE Transactions on Cognitive Communications and Networking, 3(4):563–575, Dec 2017.
K. Kim, J. Lee, and J. Choi. Deep learning based pilot allocation scheme (dl-pas) for 5g massive mimo system. IEEE Communications Letters, 22(4):828–831, April 2018.
A. Zappone, M. Debbah, and Z. Altman. Online energy-efficient power control in wireless networks by deep neural networks. In 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pages 1–5, June 2018.
H. Sun, X. Chen, Q. Shi, M. Hong, X. Fu, and N. D. Sidiropoulos. Learning to optimize: Training deep neural networks for wireless resource management. In 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pages 1–6, July 2017.
A. Agrawal, J. G. Andrews, J. M. Cioffi, and Teresa Meng. Iterative power control for imperfect successive interference cancellation. IEEE Transactions on Wireless Communications, 4(3):878–884, May 2005.
X. Chen, X. Wang, and X. Chen. Energy-efficient optimization for wireless information and power transfer in large-scale mimo systems employing energy beamforming. IEEE Wireless Communications Letters, 2(6):667–670, December 2013.
