DOI QR코드

DOI QR Code

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

비직교 다중 접속 기반 이종 네트워크에서 딥러닝 알고리즘을 이용한 사용자 및 전력 할당 기법

  • Kim, Donghyeon (School of Electronic and Electrical Engineering, Hankyong National University) ;
  • Lee, In-Ho (School of Electronic and Electrical Engineering, Hankyong National University)
  • Received : 2021.12.27
  • Accepted : 2022.01.17
  • Published : 2022.03.31

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.

본 논문에서는 하나의 매크로 기지국과 다수의 소형 기지국들로 구성된 이종 네트워크 (Heterogeneous Network, HetNET) 시스템에서 비직교 다중 접속 (Non-Orthogonal Multiple Access, NOMA) 기술을 고려한다. 여기서, NOMA 신호에 대하여 완벽한 순차적 간접 제거를 가정한다. 본 논문에서는 이러한 NOMA 기반의 이종 네트워크에서 데이터 전송률을 최대화하기 위하여 딥러닝 기반의 사용자 및 전력 할당 기법을 제안한다. 특히, 제안하는 기법은 부하 분산을 위한 심층신경망(Deep Neural Network, DNN) 기반의 사용자 할당 과정과 할당된 사용자에 대한 데이터 전송률의 최대화를 위한 DNN 기반의 전력 할당 과정을 포함한다. 기지국과 사용자간 경로 손실과 레일레이 페이딩 채널을 가정한 시뮬레이션을 통해 제안하는 기법의 성능을 평가하고, 기존의 최대 신호 대 간섭 및 잡음비(Max-Signal-to-Interference-plus-Noise Ratio, Max-SINR) 기법의 성능과 비교한다. 성능 비교를 통해서 제안된 기법이 기존의 Max-SINR 기법보다 높은 데이터 전송률을 제공하는 것을 보여준다.

Keywords

Acknowledgement

This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (Grant number: NRF-2018R1D1A1B07042499).

References

  1. 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.
  2. 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. https://doi.org/10.1109/COMST.2020.3013514
  3. 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. https://doi.org/10.1109/TPWRS.2017.2694860
  4. 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. https://doi.org/10.1109/TSP.2011.2147784
  5. 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. https://doi.org/10.1109/tsp.2018.2812733
  6. 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. https://doi.org/10.1109/tvt.2019.2951822
  7. 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.
  8. 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. https://doi.org/10.1109/access.2019.2895201
  9. 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. https://doi.org/10.1109/tvt.2017.2782726
  10. 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. https://doi.org/10.1109/tcomm.2019.2947418
  11. 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. https://doi.org/10.1109/lcomm.2018.2825444
  12. 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. https://doi.org/10.1109/lwc.2018.2864099
  13. 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. https://doi.org/10.3390/electronics9101606
  14. 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. https://doi.org/10.1109/ACCESS.2021.3109948
  15. 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.
  16. D. P. Kingma and J. Ba, "Adam: A Method for Stochastic Optimization," Dec. 2014. [Internet] Available: https://arxiv.org/abs/1412.6980.