Joint dictionary and deep learning for cosparse recovery of the millimeter wave massive MIMO channels

Abstract

The channel estimation (CE) for millimeter wave (mmW) massive multiple input multiple output (mMIMO) is a challenging task because of the important number of transmit and receive antennas, which results in high pilot overhead. In conventional CE algorithms, the channel is modeled using pre-constructed dictionary. This often leads to a suboptimal solution which cannot guarantee CE accuracy. In this paper, an iterative two-stage CE algorithm is presented. In the first stage, training measurements under different conditions are collected and it is proposed to estimate the virtual sparse mmW mMIMO channel using a deep residual learning based orthogonal approximate message passing (DRL-OAMP) algorithm from these measurements. The estimated channel is used in the second stage to learn the dictionary via a projected gradient algorithm. Simulation results show that the proposal improves the CE accuracy with low pilot overhead.