2022/12/23 by Peicong Zheng, Zheng, Peicong, Xuantao Lyu +3 · 1 citation
Engineering · #Antenna Design and Optimization #FOS: Electrical engineering #Microwave Engineering and Waveguides #Millimeter-Wave Propagation and Modeling #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2212.12214
openalex publication_date 2022/12/23 · openalex created_date 2023/01/04 · openalex updated_date 2026/07/28
In this letter, we address the problem of millimeter-Wave channel estimation in massive MIMO communication systems. Leveraging the sparsity of the mmWave channel in the beamspace, we formulate the estimation problem as a sparse signal recovery problem. To this end, we propose a deep learning based trainable proximal gradient descent network (TPGD-Net). The TPGD-Net unfolds the iterative proximal gradient descent (PGD) algorithm into a layer-wise network, with the gradient descent step size set as a trainable parameter. Additionally, we replace the proximal operator in the PGD algorithm with a neural network that exploits data-driven prior channel information to perform the proximal operation implicitly. To further enhance the transfer of feature information across layers, we introduce the cross-layer feature attention fusion module into the TPGD-Net. Our simulation results on the Saleh-Valenzuela channel model and the DeepMIMO dataset demonstrate the superior performance of TPGD-Net compared to state-of-the-art mmWave channel estimators.