Deep Joint CSI Feedback and Multiuser Precoding for MIMO OFDM Systems (2404.16289v1)
Abstract: The design of precoding plays a crucial role in achieving a high downlink sum-rate in multiuser multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. In this correspondence, we propose a deep learning based joint CSI feedback and multiuser precoding method in frequency division duplex systems, aiming at maximizing the downlink sum-rate performance in an end-to-end manner. Specifically, the eigenvectors of the CSI matrix are compressed using deep joint source-channel coding techniques. This compression method enhances the resilience of the feedback CSI information against degradation in the feedback channel. A joint multiuser precoding module and a power allocation module are designed to adjust the precoding direction and the precoding power for users based on the feedback CSI information. Experimental results demonstrate that the downlink sum-rate can be significantly improved by using the proposed method, especially in scenarios with low signal-to-noise ratio and low feedback overhead.
- J. Guo, C.-K. Wen, S. Jin, and G. Y. Li, “Overview of deep learning-based csi feedback in massive mimo systems,” IEEE Transactions on Communications, vol. 70, no. 12, pp. 8017–8045, 2022.
- C.-K. Wen, W.-T. Shih, and S. Jin, “Deep learning for massive mimo csi feedback,” IEEE Wireless Communications Letters, vol. 7, no. 5, pp. 748–751, 2018.
- J. Guo, C.-K. Wen, S. Jin, and G. Y. Li, “Convolutional neural network-based multiple-rate compressive sensing for massive mimo csi feedback: Design, simulation, and analysis,” IEEE Transactions on Wireless Communications, vol. 19, no. 4, pp. 2827–2840, 2020.
- X. Bi, S. Li, C. Yu, and Y. Zhang, “A novel approach using convolutional transformer for massive mimo csi feedback,” IEEE Wireless Communications Letters, vol. 11, no. 5, pp. 1017–1021, 2022.
- M. Chen, J. Guo, C.-K. Wen, S. Jin, G. Y. Li, and A. Yang, “Deep learning-based implicit csi feedback in massive mimo,” IEEE Transactions on Communications, vol. 70, no. 2, pp. 935–950, 2021.
- J. Xu, B. Ai, N. Wang, and W. Chen, “Deep joint source-channel coding for csi feedback: An end-to-end approach,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 1, pp. 260–273, 2022.
- M. Zhang, J. Gao, and C. Zhong, “A deep learning-based framework for low complexity multiuser mimo precoding design,” IEEE Transactions on Wireless Communications, vol. 21, no. 12, pp. 11 193–11 206, 2022.
- K. Wei, J. Xu, W. Xu, N. Wang, and D. Chen, “Distributed neural precoding for hybrid mmwave mimo communications with limited feedback,” IEEE Communications Letters, vol. 26, no. 7, pp. 1568–1572, 2022.
- F. Sohrabi, K. M. Attiah, and W. Yu, “Deep learning for distributed channel feedback and multiuser precoding in fdd massive mimo,” IEEE Transactions on Wireless Communications, vol. 20, no. 7, pp. 4044–4057, 2021.
- Z. Hu, J. Guo, G. Liu, H. Zheng, and J. Xue, “Mrfnet: A deep learning-based csi feedback approach of massive mimo systems,” IEEE Communications Letters, vol. 25, no. 10, pp. 3310–3314, 2021.
- 3GPP, “3rd generation partnership project; technical specification group radio access network; study on 3d channel model for lte (release 12),” 3GPP, Tech. Rep. 36.873 V12.7.0,, 2020.
- S. Jaeckel, L. Raschkowski, K. Borner, and L. Thiele, “QuaDRiGa-quasi deterministic radio channel generator, user manual and documentation,” Fraunhofer Heinrich Hertz Institute, Tech. Rep. v2.6.1,, 2021.
- 3GPP, “5G study on channel model for frequencies from 0.5 to 100 GHz,” 3rd Generation Partnership Project (3GPP), Technical Specification (TS) 38.901, 2020, version 16.1.0.
- Q. H. Spencer, A. L. Swindlehurst, and M. Haardt, “Zero-forcing methods for downlink spatial multiplexing in multiuser mimo channels,” IEEE transactions on signal processing, vol. 52, no. 2, pp. 461–471, 2004.