TY - GEN
T1 - Data-Driven Deep Learning-Based Rate-Splitting Multiple Access for FDD Massive MIMO-OFDM Systems with Implicit CSI
AU - Wu, Minghui
AU - Gao, Zhen
AU - Hu, Chun
AU - Li, Zhongxiang
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, the acquisition of accurate channel state information (CSI) and the implementation of spectrally efficient beamforming with limited feedback and pilot overhead present significant challenges, resulting in a substantial decrease in the performance of conventional space division multiple access (SDMA) beamforming. To address these challenges, this paper proposes a novel data-driven deep learning-based rate-splitting multiple access (RSMA) beamforming technique. The proposed approach models the crucial transmission components, including downlink pilot training, uplink pilot feedback, and RSMA beamforming, as a unified end-to-end (E2E) neural network. The network is trained in an E2E manner, eliminating the need for explicit CSI acquisition with reduced pilot and feedback overhead. Simulation results demonstrate that the proposed scheme outperforms state-of-the-art approaches.
AB - In massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, the acquisition of accurate channel state information (CSI) and the implementation of spectrally efficient beamforming with limited feedback and pilot overhead present significant challenges, resulting in a substantial decrease in the performance of conventional space division multiple access (SDMA) beamforming. To address these challenges, this paper proposes a novel data-driven deep learning-based rate-splitting multiple access (RSMA) beamforming technique. The proposed approach models the crucial transmission components, including downlink pilot training, uplink pilot feedback, and RSMA beamforming, as a unified end-to-end (E2E) neural network. The network is trained in an E2E manner, eliminating the need for explicit CSI acquisition with reduced pilot and feedback overhead. Simulation results demonstrate that the proposed scheme outperforms state-of-the-art approaches.
KW - deep learning
KW - multiple-input multiple-output (MIMO)
KW - orthogonal frequency division multiplexing (OFDM)
KW - precoding
KW - rate-splitting multiple access (RSMA)
UR - http://www.scopus.com/inward/record.url?scp=85178577382&partnerID=8YFLogxK
U2 - 10.1109/SPAWC53906.2023.10304446
DO - 10.1109/SPAWC53906.2023.10304446
M3 - Conference contribution
AN - SCOPUS:85178577382
T3 - IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
SP - 156
EP - 160
BT - 2023 IEEE 24th International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023
Y2 - 25 September 2023 through 28 September 2023
ER -