Localization in Multipath Environments via Active Sensing with Reconfigurable Intelligent Surfaces

TL;DR

This paper presents a novel RIS-based active sensing method using LSTM control to improve indoor localization in multipath environments, reducing training complexity while maintaining accuracy.

Contribution

It introduces an adaptive RIS reconfiguration approach with LSTM control to differentiate multipath signals for indoor localization.

Findings

Significantly reduces training complexity.

Maintains localization accuracy with fewer pilots.

Effective in multipath indoor environments.

Abstract

This letter investigates an uplink pilot-based wireless indoor localization problem in a multipath environment for a single-input single-output (SISO) narrowband communication system aided by reconfigurable intelligent surface (RIS). The indoor localization problem is challenging because the uplink channel consists of multiple overlapping propagation paths with varying amplitudes and phases, which are not easy to differentiate. This letter proposes the use of RIS capable of adaptively changing its reflection pattern to sense such a multiple-path environment. Toward this end, we train a long-short-term-memory (LSTM) based controller to perform adaptive sequential reconfigurations of the RIS over multiple stages and propose to group multiple pilots as input in each stage. Information from the multiple paths is captured by training the LSTM to generate multiple RIS configurations to align to the different paths within each stage. Experimental results show that the proposed approach is effective in significantly reducing training complexity while maintaining localization performance at fixed number of pilots.