Learning-Based MIMO Channel Estimation under Spectrum Efficient Pilot Allocation and Feedback

TL;DR

This paper introduces a novel approach combining a linear pilot-to-delay estimator with deep learning networks to improve MIMO channel estimation accuracy under spectrum-efficient pilot allocation, especially for time-varying channels.

Contribution

It proposes a new pilot-to-delay estimator and a heterogeneous deep learning network that enhances CSI estimation performance in massive MIMO systems with limited pilots.

Findings

P2D estimator is accurate under frequency downsampling

Unrolled optimization networks outperform prior autoencoder-based methods

MarkovNet-ISTA-ENet achieves superior asymptotic performance

Abstract

Wireless links using massive MIMO transceivers are vital for next generation wireless communications networks networks. Precoding in Massive MIMO transmission requires accurate downlink channel state information (CSI). Many recent works have effectively applied deep learning (DL) to jointly train UE-side compression networks for delay domain CSI and a BS-side decoding scheme. Vitally, these works assume that the full delay domain CSI is available at the UE, but in reality, the UE must estimate the delay domain based on a limited number of frequency domain pilots. In this work, we propose a linear pilot-to-delay (P2D) estimator that transforms sparse frequency pilots to the truncated delay CSI. We show that the P2D estimator is accurate under frequency downsampling, and we demonstrate that the P2D estimate can be effectively utilized with existing autoencoder-based CSI estimation networks. In addition to accounting for pilot-based estimates of downlink CSI, we apply unrolled optimization networks to emulate iterative solutions to compressed sensing (CS), and we demonstrate better estimation performance than prior autoencoder-based DL networks. Finally, we investigate the efficacy of trainable CS networks for in a differential encoding network for time-varying CSI estimation, and we propose a new network, MarkovNet-ISTA-ENet, comprised of both a CS network for initial CSI estimation and multiple autoencoders to estimate the error terms. We demonstrate that this heterogeneous network has better asymptotic performance than networks comprised of only one type of network.