TY - GEN
T1 - Parametric Precoding Based on Improved Dynamic Gradient Descent in Multibeam Satellite Communications
AU - Wang, Jiayu
AU - Yao, Rugui
AU - Xu, Donghui
AU - Fan, Ye
AU - Zuo, Xiaoya
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Precoding and beamforming have been studied in multibeam satellite. In this paper, we propose a precoding method based on dynamic gradient descent, which is suitable for large array antenna and multiuser multibeam satellite environment. This method doesn't need complex vector or matrix design because of combining zero-forcing and maximum ratio transmission. And gradient descent algorithm is used to solve this combining zero-forcing and maximum ratio transmission model. Meanwhile, the convergence of gradient descent is also proved. The simulation results show that the improved gradient descent based on parametric design can get higher sum rate, whether it is in the case of multiple users with massive antennas or small users with a few number of antennas. Meanwhile, for the gradient descent convergence problem, we propose a dynamic adaptive learning rate updating method, which has faster and better convergence performance than traditional gradient descent algorithm.
AB - Precoding and beamforming have been studied in multibeam satellite. In this paper, we propose a precoding method based on dynamic gradient descent, which is suitable for large array antenna and multiuser multibeam satellite environment. This method doesn't need complex vector or matrix design because of combining zero-forcing and maximum ratio transmission. And gradient descent algorithm is used to solve this combining zero-forcing and maximum ratio transmission model. Meanwhile, the convergence of gradient descent is also proved. The simulation results show that the improved gradient descent based on parametric design can get higher sum rate, whether it is in the case of multiple users with massive antennas or small users with a few number of antennas. Meanwhile, for the gradient descent convergence problem, we propose a dynamic adaptive learning rate updating method, which has faster and better convergence performance than traditional gradient descent algorithm.
KW - beamforming
KW - Gradient descent algorithm
KW - multibeam satellite
KW - precoding
UR - http://www.scopus.com/inward/record.url?scp=85162636145&partnerID=8YFLogxK
U2 - 10.1109/WOCC58016.2023.10139341
DO - 10.1109/WOCC58016.2023.10139341
M3 - 会议稿件
AN - SCOPUS:85162636145
T3 - 32nd Wireless and Optical Communications Conference, WOCC 2023
BT - 32nd Wireless and Optical Communications Conference, WOCC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 32nd Wireless and Optical Communications Conference, WOCC 2023
Y2 - 5 May 2023 through 6 May 2023
ER -