Quantization Adaptor for Bit-Level Deep Learning-Based Massive MIMO CSI Feedback

TL;DR

This paper introduces a novel adaptor-assisted quantization method for deep learning-based CSI feedback in massive MIMO systems, significantly improving quantization accuracy and reconstruction with minimal additional cost.

Contribution

It proposes a network-aided adaptor and training scheme that adaptively enhances quantization and reconstruction accuracy in DL-based CSI feedback.

Findings

Achieves better quantization accuracy than state-of-the-art methods.

Improves reconstruction performance with less or no extra cost.

Provides an easy-to-implement, pluggable adaptor scheme.

Abstract

In massive multiple-input multiple-output (MIMO) systems, the user equipment (UE) needs to feed the channel state information (CSI) back to the base station (BS) for the following beamforming. But the large scale of antennas in massive MIMO systems causes huge feedback overhead. Deep learning (DL) based methods can compress the CSI at the UE and recover it at the BS, which reduces the feedback cost significantly. But the compressed CSI must be quantized into bit streams for transmission. In this paper, we propose an adaptor-assisted quantization strategy for bit-level DL-based CSI feedback. First, we design a network-aided adaptor and an advanced training scheme to adaptively improve the quantization and reconstruction accuracy. Moreover, for easy practical employment, we introduce the expert knowledge of data distribution and propose a pluggable and cost-free adaptor scheme. Experiments show that compared with the state-of-the-art feedback quantization method, this adaptor-aided quantization strategy can achieve better quantization accuracy and reconstruction performance with less or no additional cost. The open-source codes are available at https://github.com/zhang-xd18/QCRNet.