Device-Free Localization Using Multi-Link MIMO Channels in Distributed Antenna Networks

TL;DR

This paper introduces a device-free localization method using distributed MIMO channels in antenna networks, achieving sub-meter accuracy and robustness in indoor environments, suitable for future 6G integrated sensing and communication systems.

Contribution

It proposes a novel DFL framework leveraging multi-link MIMO channels in distributed antenna networks, with a prototype demonstrating high accuracy and robustness in complex indoor scenarios.

Findings

Achieves sub-meter localization accuracy in indoor environments.

Maintains robust performance under multipath conditions.

Bayesian optimization improves image reconstruction and target estimation.

Abstract

Targeting integrated sensing and communication (ISAC) in future 6G radio access networks (RANs), this paper presents a novel device-free localization (DFL) framework based on distributed antenna networks (DANs). In the proposed approach, radio tomographic imaging (RTI) leverages the spatial and temporal diversity of multi-link multiple-input multiple-output (MIMO) channels in DANs to achieve accurate localization. Furthermore, a prototype system was developed using software-defined radios (SDRs) operating in the sub-6 GHz band, and comprehensive evaluations were conducted under indoor conditions involving varying node densities and target types. The results demonstrate that the framework achieves sub-meter localization accuracy in most scenarios and maintains robust performance under complex multipath environments. In addition, the use of Bayesian optimization to fine-tune key parameters, such as sparsity and path thickness, led to significant improvements in image reconstruction quality and target estimation accuracy. These results demonstrate the feasibility and effectiveness of DAN-based DFL as a scalable and infrastructure-compatible ISAC solution, capable of delivering accurate, passive localization without dedicated sensing hardware.