A General Sensing-assisted Channel Estimation Framework in Distributed MIMO Network

TL;DR

This paper introduces a comprehensive sensing-assisted channel estimation framework for distributed MIMO networks, enabling accurate channel estimation in dynamic indoor environments with moving targets, surpassing traditional methods.

Contribution

It proposes a novel framework that jointly senses moving targets and incorporates ray tracing for improved channel estimation in DMIMO networks, addressing non-LoS scenarios.

Findings

Achieves significantly higher channel estimation accuracy.

Enhances downlink throughput compared to LS estimation.

Validates feasibility for stringent accuracy requirements.

Abstract

In 6G communications, it is envisioned to equip the traditional access point (AP) with sensing capability to fully benefit the existing wireless communication infrastructures. Thus, sensing-assisted communication has attracted significant attention from both industry and academia. However, most existing works focused on sensing-assisted communication in line-of-sight (LoS) scenarios due to sensing limitations, where the sensing target (ST) and communication user equipment (UE) remain the same. In this paper, we propose a general sensing-assisted channel estimation framework in the distributed multiple-input and multiple-output (DMIMO) network and consider a scenario where the ST and UE are different entities. In addition, ST is a moving target (e.g. a robot) which causes channels between APs and UEs to vary due to changes in the reflection paths of the indoor environment. Therefore, we let multiple APs to jointly sense the position of the ST, which will be incorporated in a Ray tracing model to obtain a more accurate estimate of the channels from APs to UEs for both the LoS and non-line-of-sight (NLoS) scenarios. Simulation results demonstrate that our proposed sensing-assisted communication framework achieves a much higher channel estimation accuracy and downlink throughput compared to the traditional least-square (LS) channel estimation. More importantly, the feasibility of the proposed framework has been validated to guarantee the stringent channel estimation accuracy requirement in the DMIMO network.