Deep Learning for Joint Channel Estimation and Feedback in Massive MIMO Systems

TL;DR

This paper introduces a deep learning framework for joint channel estimation and feedback in FDD massive MIMO systems, significantly reducing feedback overhead while maintaining high accuracy in channel reconstruction.

Contribution

It proposes two novel neural networks for explicit and implicit channel estimation and feedback, improving robustness and efficiency in massive MIMO systems.

Findings

High-quality channel reconstruction demonstrated in simulations

Robust performance across different SNRs and environments

Effective quantization module reduces data size without sacrificing accuracy

Abstract

The great potentials of massive Multiple-Input Multiple-Output (MIMO) in Frequency Division Duplex (FDD) mode can be fully exploited when the downlink Channel State Information (CSI) is available at base stations. However, the accurate CSI is difficult to obtain due to the large amount of feedback overhead caused by massive antennas. In this paper, we propose a deep learning based joint channel estimation and feedback framework, which comprehensively realizes the estimation, compression, and reconstruction of downlink channels in FDD massive MIMO systems. Two networks are constructed to perform estimation and feedback explicitly and implicitly. The explicit network adopts a multi-Signal-to-Noise-Ratios (SNRs) technique to obtain a single trained channel estimation subnet that works well with different SNRs and employs a deep residual network to reconstruct the channels, while the implicit network directly compresses pilots and sends them back to reduce network parameters. Quantization module is also designed to generate data-bearing bitstreams. Simulation results show that the two proposed networks exhibit excellent performance of reconstruction and are robust to different environments and quantization errors.