TY - GEN
T1 - A Two-Stage Majorization-Minimization Based Beamforming for Downlink Massive MIMO
AU - Xu, Qian
AU - Sun, Jianyong
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In this paper, we investigate the transmit beamforming design for weighted sum-rate maximization in massive multiple-input multiple-output (MIMO) downlink systems. Currently, the most popular algorithm for this scenario is the weighted minimum mean square error (WMMSE) algorithm. We propose a two-stage majorization-minimization (MM) based beamforming (dubbed TMMBF) which transforms the weighted sum-rate maximization problem into a quadratic convex problem by utilizing the MM method twice. The proposed algorithm converges to a stationary point of the weighted sum-rate maximization problem. Interestingly, we find that the WMMSE algorithm is a special case of the TMMBF algorithm, thus unifying the WMMSE algorithm into the MM framework for the first time. In addition, the surrogate function of TMMBF is tighter than that of WMMSE, resulting in faster convergence of the TMMBF algorithm. The simulation results on 3GPP channel models generated from Quadriga show that the TMMBF algorithm has better performance and faster numerical convergence compared to the WMMSE algorithm.
AB - In this paper, we investigate the transmit beamforming design for weighted sum-rate maximization in massive multiple-input multiple-output (MIMO) downlink systems. Currently, the most popular algorithm for this scenario is the weighted minimum mean square error (WMMSE) algorithm. We propose a two-stage majorization-minimization (MM) based beamforming (dubbed TMMBF) which transforms the weighted sum-rate maximization problem into a quadratic convex problem by utilizing the MM method twice. The proposed algorithm converges to a stationary point of the weighted sum-rate maximization problem. Interestingly, we find that the WMMSE algorithm is a special case of the TMMBF algorithm, thus unifying the WMMSE algorithm into the MM framework for the first time. In addition, the surrogate function of TMMBF is tighter than that of WMMSE, resulting in faster convergence of the TMMBF algorithm. The simulation results on 3GPP channel models generated from Quadriga show that the TMMBF algorithm has better performance and faster numerical convergence compared to the WMMSE algorithm.
KW - Beamforming
KW - majorization-minimization
KW - massive MIMO
KW - weighted MMSE
UR - https://www.scopus.com/pages/publications/85159786585
U2 - 10.1109/WCNC55385.2023.10118795
DO - 10.1109/WCNC55385.2023.10118795
M3 - 会议稿件
AN - SCOPUS:85159786585
T3 - IEEE Wireless Communications and Networking Conference, WCNC
BT - 2023 IEEE Wireless Communications and Networking Conference, WCNC 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE Wireless Communications and Networking Conference, WCNC 2023
Y2 - 26 March 2023 through 29 March 2023
ER -