CovNet: Covariance Information-Assisted CSI Feedback for FDD Massive MIMO Systems

TL;DR

This paper introduces CovNet, a novel neural network-based CSI feedback scheme for FDD massive MIMO systems that utilizes covariance information to improve reconstruction accuracy over existing methods.

Contribution

The paper proposes a new CovNet architecture that incorporates covariance information processing and joint angle-delay domain features for enhanced CSI feedback in massive MIMO systems.

Findings

Outperforms state-of-the-art schemes across all compression ratios

Effectively leverages covariance information for better CSI reconstruction

Utilizes CNN and Transformer architectures for improved performance

Abstract

In this paper, we propose a novel covariance information-assisted channel state information (CSI) feedback scheme for frequency-division duplex (FDD) massive multi-input multi-output (MIMO) systems. Unlike most existing CSI feedback schemes, which rely on instantaneous CSI only, the proposed CovNet leverages CSI covariance information to achieve high-performance CSI reconstruction, primarily consisting of convolutional neural network (CNN) and Transformer architecture. To efficiently utilize covariance information, we propose a covariance information processing procedure and sophisticatedly design the covariance information processing network (CIPN) to further process it. Moreover, the feed-forward network (FFN) in CovNet is designed to jointly leverage the 2D characteristics of the CSI matrix in the angle and delay domains. Simulation results demonstrate that the proposed network effectively leverages covariance information and outperforms the state-of-the-art (SOTA) scheme across the full compression ratio (CR) range.