Deep Unfolded Hybrid Beamforming in Reconfigurable Intelligent Surface Aided mmWave MIMO-OFDM Systems
Deep Unfolded Hybrid Beamforming in Reconfigurable Intelligent Surface Aided mmWave MIMO-OFDM Systems
Kuan-Ming Chen,Hsin-Yuan Chang,Ronald Y. Chang,W. Chung
TLDR
The proposed deep-unfolded WMMSE-MO algorithm demonstrates superior SE performance, convergence speed, and computational efficiency compared to both its counterpart without deep unfolding and previous methods.
Abstract
This letter considers a millimeter-wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) transceiver system assisted by a reconfigurable intelligent surface (RIS). The goal is to jointly design transceiver hybrid beamforming and RIS phase shifts to maximize spectral efficiency (SE). We adapt the weighted minimum mean square error manifold optimization (WMMSE-MO) algorithm to the RIS-assisted system, and further deep-unfold it with neural networks to alleviate the algorithm’s computational complexity and expedite its convergence. The proposed deep-unfolded WMMSE-MO algorithm demonstrates superior SE performance, convergence speed, and computational efficiency compared to both its counterpart without deep unfolding and previous methods.