Deep Learning based Antenna Selection and CSI Extrapolation in Massive MIMO Systems

TL;DR

This paper introduces a deep learning framework for antenna selection and CSI extrapolation in massive MIMO systems, significantly reducing training overhead by leveraging correlations among antennas.

Contribution

It proposes a novel deep neural network for CSI extrapolation and a differentiable antenna selection scheme, improving efficiency over traditional methods.

Findings

ADEN outperforms traditional fully connected networks

ASN-based antenna selection surpasses uniform selection

Reduces training overhead in massive MIMO systems

Abstract

A critical bottleneck of massive multiple-input multiple-output (MIMO) system is the huge training overhead caused by downlink transmission, like channel estimation, downlink beamforming and covariance observation. In this paper, we propose to use the channel state information (CSI) of a small number of antennas to extrapolate the CSI of the other antennas and reduce the training overhead. Specifically, we design a deep neural network that we call an antenna domain extrapolation network (ADEN) that can exploit the correlation function among antennas. We then propose a deep learning (DL) based antenna selection network (ASN) that can select a limited antennas for optimizing the extrapolation, which is conventionally a type of combinatorial optimization and is difficult to solve. We trickly designed a constrained degradation algorithm to generate a differentiable approximation of the discrete antenna selection vector such that the back-propagation of the neural network can be guaranteed. Numerical results show that the proposed ADEN outperforms the traditional fully connected one, and the antenna selection scheme learned by ASN is much better than the trivially used uniform selection.