Integrated Near Field Sensing and Communications Using Unitary Approximate Message Passing Based Matrix Factorization

TL;DR

This paper introduces a low-complexity Bayesian matrix factorization method based on unitary approximate message passing to enhance near-field localization and communication in integrated sensing and communication systems with large antenna arrays.

Contribution

It formulates near-field localization and communication as a structured matrix factorization problem and develops a novel UAMP-MF algorithm for efficient joint localization and signal detection.

Findings

UAMP-MF outperforms existing methods in simulation.

The approach effectively exploits matrix structures for improved accuracy.

Low computational complexity compared to traditional algorithms.

Abstract

Due to the utilization of large antenna arrays at base stations (BSs) and the operations of wireless communications in high frequency bands, mobile terminals often find themselves in the near-field of the array aperture. In this work, we address the signal processing challenges of integrated near-field localization and communication in uplink transmission of an integrated sensing and communication (ISAC) system, where the BS performs joint near-field localization and signal detection (JNFLSD). We show that JNFLSD can be formulated as a matrix factorization (MF) problem with proper structures imposed on the factor matrices. Then, leveraging the variational inference (VI) and unitary approximate message passing (UAMP), we develop a low complexity Bayesian approach to MF, called UAMP-MF, to handle a generic MF problem. We then apply the UAMP-MF algorithm to solve the JNFLSD problem, where the factor matrix structures are fully exploited. Extensive simulation results are provided to demonstrate the superior performance of the proposed method.