Transformer-assisted Parametric CSI Feedback for mmWave Massive MIMO Systems

TL;DR

This paper introduces a deep learning-based parametric CSI feedback method for mmWave massive MIMO systems that significantly reduces feedback overhead by compressing channel information into geometric parameters, improving accuracy and performance.

Contribution

It proposes a novel parametric CSI feedback technique leveraging deep learning to extract geometric channel parameters, reducing feedback overhead and enhancing channel reconstruction accuracy.

Findings

Outperforms conventional methods in NMSE and BER

Effectively reduces CSI feedback overhead

Suppresses channel quantization error

Abstract

As a key technology to meet the ever-increasing data rate demand in beyond 5G and 6G communications, millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems have gained much attention recently.To make the most of mmWave massive MIMO systems, acquisition of accurate channel state information (CSI) at the base station (BS) is crucial. However, this task is by no means easy due to the CSI feedback overhead induced by the large number of antennas. In this paper, we propose a parametric CSI feedback technique for mmWave massive MIMO systems. Key idea of the proposed technique is to compress the mmWave MIMO channel matrix into a few geometric channel parameters (e.g., angles, delays, and path gains). Due to the limited scattering of mmWave signal, the number of channel parameters is much smaller than the number of antennas, thereby reducing the CSI feedback overhead significantly. Moreover, by exploiting the deep learning (DL) technique for the channel parameter extraction and the MIMO channel reconstruction, we can effectively suppress the channel quantization error. From the numerical results, we demonstrate that the proposed technique outperforms the conventional CSI feedback techniques in terms of normalized mean square error (NMSE) and bit error rate (BER).