Integrated Sensing, User Location and Orientation Estimation in RIS-Assisted Dynamic Rich Scattering Environment

TL;DR

This paper presents a novel adaptive sensing approach using RIS and deep learning to accurately localize and estimate the orientation of users in complex indoor rich-scattering environments.

Contribution

It introduces a biLSTM-based controller for adaptive environment sensing and beamforming design in RIS-assisted MIMO systems for improved localization accuracy.

Findings

Achieves low localization error in various rich-scattering scenarios.

Demonstrates robustness against moving scattering objects and different RIS configurations.

Effectively integrates environment sensing with beamforming for user localization.

Abstract

This paper investigates an uplink user equipment (UE) location and orientation estimation problem in an indoor rich-scattering environment (RSE) for a multiple-input-multiple-output (MIMO) narrowband reconfigurable intelligent surfaces (RIS)-assisted communication system. The localization problem in RSE is challenging as the uplink pilot signal undergoes multiple interactions with the RIS and dynamic scattering objects (SOs). This paper proposes an approach where base station (BS) adaptively senses the environment with the help of RIS. Based on this sensing, it sequentially designs RIS configuration, BS beamforming and UE beamforming vectors, using the sequence of pilot transmissions from the UE to the BS, with an objective of progressively focusing them onto the UE. Towards this end, we train a bidirectional long-short term memory (biLSTM) network based controller to capture the temporal dependencies between measurements to first adaptively sense the RSE and then design RIS, BS and UE beamforming vectors to localize the UE. We evaluate the proposed approach under various RSE conditions such as various distributed RIS installations, varying number of randomly moving SOs and sensing RIS elements. Simulation results illustrate that it effectively enables adaptive sensing to achieve low localization error with robustness in various RSEs.