RIS-Assisted Sensing: A Nested Tensor Decomposition-Based Approach

TL;DR

This paper introduces a tensor decomposition method for RIS-assisted monostatic MIMO sensing, enabling joint estimation of target parameters with high accuracy and low complexity.

Contribution

It proposes a novel nested tensor decomposition approach leveraging multidimensional signal structure for improved target sensing in RIS-assisted MIMO systems.

Findings

Accurate estimation of delay, Doppler, and angle parameters.

Low-complexity tensor-based algorithm outperforms traditional methods.

Effective joint parameter estimation in simulated RIS-assisted sensing scenarios.

Abstract

We study a monostatic multiple-input multiple-output sensing scenario assisted by a reconfigurable intelligent surface using tensor signal modeling. We propose a method that exploits the intrinsic multidimensional structure of the received echo signal, allowing us to recast the target sensing problem as a nested tensor-based decomposition problem to jointly estimate the delay, Doppler, and angular information of the target. We derive a two-stage approach based on the alternating least squares algorithm followed by the estimation of the signal parameters via rotational invariance techniques to extract the target parameters. Simulation results show that the proposed tensor-based algorithm yields accurate estimates of the sensing parameters with low complexity.