How Proximal gradient descent works part1(Machine Learning Optimization)

Abstract: In this letter, we focus on the problem of millimeter-Wave channels estimation in massive MIMO communication systems. Inspired by the sparsity of mmWave MIMO channel in the angular domain, we formulate the estimation problem as a sparse signal recovery problem. We propose a deep learning based trainable proximal gradient descent network (TPGD-Net) for mmWave channel estimation. Specifically, we unfold the iterative proximal gradient descent (PGD) algorithm into a layer-wise network. Different from the PGD algorithm, the gradient descent step size in TPGD-Net is set as a trainable parameter.