Browse > Article
http://dx.doi.org/10.6109/jkiice.2022.26.3.430

User Association and Power Allocation Scheme Using Deep Learning Algorithmin Non-Orthogonal Multiple Access Based Heterogeneous Networks  

Kim, Donghyeon (School of Electronic and Electrical Engineering, Hankyong National University)
Lee, In-Ho (School of Electronic and Electrical Engineering, Hankyong National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.3, 2022 , pp. 430-435 More about this Journal
Abstract
In this paper, we consider the non-orthogonal multiple access (NOMA) technique in the heterogeneous network (HetNET) consisting of a single macro base station (BS) and multiple small BSs, where the perfect successive interference cancellation is assumed for the NOMA signals. In this paper, we propose a deep learning-based user association and power allocation scheme to maximize the data rate in the NOMA-based HetNET. In particular, the proposed scheme includes the deep neural network (DNN)-based user association process for load balancing and the DNN-based power allocation process for data-rate maximization. Through the simulation assuming path loss and Rayleigh fading channels between BSs and users, the performance of the proposed scheme is evaluated, and it is compared with the conventional maximum signal-to-interference-plus-noise ratio (Max-SINR) scheme. Through the performance comparison, we show that the proposed scheme provides better sum rate performance than the conventional Max-SINR scheme.
Keywords
Deep learning; Heterogeneous network; Non-orthogonal multiple access; Power allocation; User association;
Citations & Related Records
연도 인용수 순위
  • Reference
1 W. Saetan and S. Thipchaksurat, "Power Allocation for Sum Rate Maximization in 5G NOMA System with Imperfect SIC: A Deep Learning Approach," in Proceedings of the 2019 4th International Conference on Information Technology (InCIT), pp. 195-198, 2019.
2 D. Kim and I. -H. Lee, "Deep Learning-Based Power Control Scheme for Perfect Fairness in Device-to-Device Communication Systems," Electronics, vol. 9, no. 10, pp. 1606, Oct. 2020.   DOI
3 V. W. S. Wong, R. Schober, D. W. K. Ng, and L. -C. Wang, Key Technologies for 5G Wireless Systems. U.K.: Cambridge Univ. Press, 2017.
4 O. Maraqa, A. S. Rajasekaran, S. Al-Ahmadi, H. Yanikomeroglu and S. M. Sait, "A Survey of Rate-Optimal Power Domain NOMA With Enabling Technologies of Future Wireless Networks," IEEE Communications Surveys & Tutorials, vol. 22, no. 4, pp. 2192-2235, Aug. 2020.   DOI
5 W. Lu, M. Liu, S. Lin, and L. Li, "Fully Decentralized Optimal Power Flow of Multi-Area Interconnected Power Systems Based on Distributed Interior Point Method," IEEE Transactions on Power Systems, vol. 33, no. 1, pp. 901-910, Jan. 2018.   DOI
6 Q. Shi, M. Razaviyayn, Z. -Q. Luo, and C. He, "An Iteratively Weighted MMSE Approach to Distributed Sum-Utility Maximization for a MIMO Interfering Broadcast Channel," IEEE Transactions on Signal Processing, vol. 59, no. 9, pp. 4331-4340, Sep. 2011.   DOI
7 W. Lee, M. Kim, and D. -H. Cho, "Deep Power Control: Transmit Power Control Scheme Based on Convolutional Neural Network," IEEE Communications Letters, vol. 22, no. 6, pp. 1276-1279, Jun. 2018.   DOI
8 W. Lee, M. Kim, and D. -H. Cho, "Transmit Power Control Using Deep Neural Network for Underlay Device-to-Device Communication," IEEE Wireless Communications Letters, vol. 8, no. 1, pp. 141-144, Feb. 2019.   DOI
9 S. Ioffe, and C. Szegedy, "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift," in Proceedings of the 32nd International Conference on Machine Learning, Lille: France, vol. 37, pp. 448-456, 2015.
10 D. P. Kingma and J. Ba, "Adam: A Method for Stochastic Optimization," Dec. 2014. [Internet] Available: https://arxiv.org/abs/1412.6980.
11 K. Shen and W. Yu, "Fractional Programming for Communication Systems-Part I: Power Control and Beamforming," IEEE Transactions on Signal Processing, vol. 66, no. 10, pp. 2616-2630, May. 2018.   DOI
12 D. Kim, H. Jung, and I.-H. Lee, "Deep Learning-Based Power Control Scheme With Partial Channel Information in Overlay Device-to-Device Communication Systems," IEEE Access, vol. 9, pp. 122125-122137, Sep. 2021.   DOI
13 J. Luo, J. Tang, D. K. C. So, G. Chen, K. Cumanan, and J. A. Chambers, "A Deep Learning-Based Approach to Power Minimization in Multi-Carrier NOMA With SWIPT," IEEE Access, vol. 7, pp. 17450-17460, Jan. 2019.   DOI
14 L. Xiao, Y. Li, C. Dai, H. Dai, and H. V. Poor, "Reinforcement Learning-Based NOMA Power Allocation in the Presence of Smart Jamming," IEEE Transactions on Vehicular Technology, vol. 67, no. 4, pp. 3377-3389, Apr. 2018.   DOI
15 K. N. Doan, M. Vaezi, W. Shin, H. V. Poor, H. Shin, and T. Q. S. Quek, "Power Allocation in Cache-Aided NOMA Systems: Optimization and Deep Reinforcement Learning Approaches," IEEE Transactions on Communications, vol. 68, no. 1, pp. 630-644, Jan. 2020.   DOI
16 N. Yang, H. Zhang, K. Long, H. -Y. Hsieh, and, J. Liu, "Deep Neural Network for Resource Management in NOMA Networks," IEEE Transactions on Vehicular Technology, vol. 69, no. 1, pp. 876-886, Jan. 2020.   DOI