Near-Field Integrated Imaging and Communication in Distributed MIMO Networks

TL;DR

This paper introduces a comprehensive framework for near-field wireless imaging and communication in distributed MIMO systems, employing novel algorithms for high-resolution small object imaging indoors and large-scale environment reconstruction outdoors.

Contribution

It presents a unified approach combining RMA and SBL algorithms tailored for different scenarios, addressing non-isotropic near-field effects and multi-view targets in distributed MIMO networks.

Findings

High-resolution imaging of small objects achieved indoors.

Accurate large-scale environment reconstruction outdoors.

Effective handling of non-isotropic near-field channels.

Abstract

In this work, we propose a general framework for wireless imaging in distributed MIMO wideband communication systems, considering multi-view non-isotropic targets and near-field propagation effects. For indoor scenarios where the objective is to image small-scale objects with high resolution, we propose a range migration algorithm (RMA)-based scheme using three kinds of array architectures: the full array, boundary array, and distributed boundary array. With non-isotropic near-field channels, we establish the Fourier transformation (FT)-based relationship between the imaging reflectivity and the distributed spatial-domain signals and discuss the corresponding theoretical properties. Next, for outdoor scenarios where the objective is to reconstruct the large-scale three-dimensional (3D) environment with coarse resolution, we propose a sparse Bayesian learning (SBL)-based algorithm to solve the multiple measurement vector (MMV) problem, which further addresses the non-isotropic reflectivity across different subcarriers. Numerical results demonstrate the effectiveness of the proposed algorithms in acquiring high-resolution small objects and accurately reconstructing large-scale environments.