Multi-Task Learning for Multi-User CSI Feedback

TL;DR

This paper introduces multi-task learning techniques to improve deep learning-based CSI feedback models for multi-user massive MIMO systems, enhancing performance and system sum rate.

Contribution

It proposes novel multi-task training methods that enable existing CSI feedback models to perform better in multi-user scenarios with varying channels.

Findings

CSINet trained with STNet shows 39% performance improvement.

System sum rate increases by 0.07 bps/Hz.

Multi-task learning enhances multi-user CSI feedback effectiveness.

Abstract

Deep learning-based massive MIMO CSI feedback has received a lot of attention in recent years. Now, there exists a plethora of CSI feedback models mostly based on auto-encoders (AE) architecture with an encoder network at the user equipment (UE) and a decoder network at the gNB (base station). However, these models are trained for a single user in a single-channel scenario, making them ineffective in multi-user scenarios with varying channels and varying encoder models across the users. In this work, we address this problem by exploiting the techniques of multi-task learning (MTL) in the context of massive MIMO CSI feedback. In particular, we propose methods to jointly train the existing models in a multi-user setting while increasing the performance of some of the constituent models. For example, through our proposed methods, CSINet when trained along with STNet has seen a $39\%$ increase in performance while increasing the sum rate of the system by $0.07bps/Hz$.