Lightweight and Flexible Deep Equilibrium Learning for CSI Feedback in FDD Massive MIMO

TL;DR

This paper introduces a lightweight, flexible deep equilibrium model for CSI feedback in FDD massive MIMO systems, reducing complexity while maintaining performance and allowing adjustable accuracy-efficiency trade-offs.

Contribution

It proposes an implicit equilibrium block with shared parameters to mimic infinite-depth neural networks, enabling a lightweight and adaptable CSI feedback method.

Findings

Achieves comparable performance to benchmarks with less complexity

Allows runtime adjustment of accuracy and efficiency

Demonstrates effectiveness through simulation results

Abstract

In frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, downlink channel state information (CSI) needs to be sent back to the base station (BS) by the users, which causes prohibitive feedback overhead. In this paper, we propose a lightweight and flexible deep learning-based CSI feedback approach by capitalizing on deep equilibrium models. Different from existing deep learning-based methods that stack multiple explicit layers, we propose an implicit equilibrium block to mimic the behavior of an infinite-depth neural network. In particular, the implicit equilibrium block is defined by a fixed-point iteration and the trainable parameters in different iterations are shared, which results in a lightweight model. Furthermore, the number of forward iterations can be adjusted according to users' computation capability, enabling a flexible accuracy-efficiency trade-off. Simulation results will show that the proposed design obtains a comparable performance as the benchmarks but with much-reduced complexity and permits an accuracy-efficiency trade-off at runtime.