Deep Learning for Efficient CSI Feedback in Massive MIMO: Adapting to New Environments and Small Datasets

TL;DR

This paper introduces a lightweight, adaptable deep learning framework for efficient CSI feedback in massive MIMO systems, capable of handling new environments and small datasets through domain knowledge-based augmentation and model translation.

Contribution

It proposes a novel scenario-adaptive CSI feedback architecture with a lightweight translation module and a domain knowledge-based data augmentation strategy.

Findings

Effective CSI feedback with limited measurements in unseen environments

Enhanced model efficiency and accuracy through integrated design

Successful adaptation to new CSI scenarios with small datasets

Abstract

Deep learning (DL)-based channel state information (CSI) feedback has shown promising potential to improve spectrum efficiency in massive MIMO systems. However, practical DL approaches require a sizeable CSI dataset for each scenario, and require large storage or updating bandwidth for multiple learned models. To overcome this costly barrier, we develop a solution for efficient training and deployment enhancement of DL-based CSI feedback by exploiting a lightweight translation model to cope with new CSI environments and by proposing novel dataset augmentation based on domain knowledge. Specifically, we first develop a deep unfolding CSI feedback network, SPTM2-ISTANet+, which employs spherical normalization to address the challenge of path loss variation. We also introduce an integration of a trainable measurement matrix and residual CSI recovery blocks within SPTM2-ISTANet+ to improve efficiency and accuracy. Using SPTM2-ISTANet+ as the anchor feedback model, we propose an efficient scenario-adaptive CSI feedback architecture. This new CSI-TransNet exploits a plug-in module for CSI translation consisting of a sparsity aligning function and lightweight DL module to reuse pretrained models in unseen environments. To work with small datasets, we propose a lightweight and general augmentation strategy based on domain knowledge. Test results demonstrate the efficacy and efficiency of the proposed solution for accurate CSI feedback given limited measurements for unseen CSI environments.