Joint Near Field Uplink Communication and Localization Using Message Passing-Based Sparse Bayesian Learning

TL;DR

This work addresses joint near-field uplink communication and localization in ISAC systems with large base-station arrays, where NF effects invalidate plane-wave models. It reformulates the problem as sparse signal recovery on a grid and develops a message-passing based sparse Bayesian learning algorithm that leverages unitary AMP estimators within a factor-graph framework. The approach features a two-stage process: first localize users by inferring sparse grid supports and precisions, then perform differential demodulation on a reduced active set to recover transmitted symbols, using forward/backward message passing augmented by Gaussian-EP approximations. Empirical results show high localization accuracy at moderate SNR, competitive BER performance, and pilotless operation via differential modulation, highlighting the method's practicality for NF ISAC in dense, high-frequency networks.

Abstract

This work deals with the problem of uplink communication and localization in an integrated sensing and communication system, where users are in the near field (NF) of antenna aperture due to the use of high carrier frequency and large antenna arrays at base stations. We formulate joint NF signal detection and localization as a problem of recovering signals with a sparse pattern. To solve the problem, we develop a message passing based sparse Bayesian learning (SBL) algorithm, where multiple unitary approximate message passing (UAMP)-based sparse signal estimators work jointly to recover the sparse signals with low complexity. Simulation results demonstrate the effectiveness of the proposed method.

Figures (6)

• Figure 1: Illustration of the ISAC system.
• Figure 2: Factor graph representation of \ref{['eq:nfcfactor']}
• Figure 3: Factor graph representation of \ref{['eq:comfactor']}
• Figure 4: Localization accuracy comparison of the algorithms.
• Figure 5: BER comparisons versus SNR