Uplink Networked Sensing via Multiuser Correlation Exploitation

TL;DR

This paper introduces a novel multi-user uplink sensing method in mobile networks that leverages cross-user correlation and delay sparsity to improve resolution and robustness in challenging conditions.

Contribution

It formulates an asynchronous multi-user OFDMA sensing model and develops a joint delay, Doppler, and angle estimation approach exploiting common sparsity and correlation.

Findings

Outperforms per-user processing in low SNR conditions

Effective suppression of uplink offsets improves sensing accuracy

Enhances resolution with limited subcarrier resources

Abstract

In this correspondence, we investigate networked sensing in perceptive mobile networks under a bistatic multi-transmitter single-receiver uplink topology, where multiple user equipments (UEs) transmit signals over orthogonal frequency-division multiple access (OFDMA) resources and a single base station performs joint sensing. Uplink clock asynchronism introduces offsets that destroy inter-packet coherence and hinder high-resolution sensing, while multi-user observations exhibit exploitable cross-user correlation. We therefore formulate an asynchronous multi-user uplink OFDMA sensing model and exploit common delay-cluster sparsity across UEs. A line-of-sight (LoS)-referenced calibration first suppresses the offsets, after which a shared-private delay-domain sparse Bayesian learning (SBL) model is used for delay support recovery and user grouping. Doppler and angle of arrival are then estimated from temporal and spatial phase differences. Simulation results show that the proposed scheme outperforms per-user processing, particularly under limited subcarrier budgets and in low signal-to-noise ratio (SNR) regimes.