Uplink Sensing in Perceptive Mobile Networks with Asynchronous Transceivers

TL;DR

This paper introduces an uplink sensing scheme for perceptive mobile networks with asynchronous transceivers, addressing challenges of timing and frequency offsets to enhance sensing accuracy in target detection.

Contribution

It proposes a novel scheme combining cross-antenna cross-correlation, mirrored-MUSIC, and high-resolution AoA estimation to resolve sensing ambiguities and improve accuracy in asynchronous PMNs.

Findings

Effective removal of sensing ambiguity caused by TOs and CFOs.

Significantly improved target delay and Doppler frequency estimation accuracy.

Validated through numerical simulations demonstrating scheme effectiveness.

Abstract

Perceptive mobile network (PMN) is a recently proposed next-generation network that integrates radar sensing into communication. One major challenge for realizing sensing in PMNs is how to deal with spatially-separated asynchronous transceivers. The asynchrony between sensing receiver and transmitter will cause both timing offsets (TOs) and carrier frequency offsets (CFOs) and lead to degraded sensing accuracy in both ranging and velocity measurements. In this paper, we propose an uplink sensing scheme for PMNs with asynchronous transceivers, targeting at resolving the sensing ambiguity and improving the sensing accuracy. We first adopt a cross-antenna cross-correlation (CACC) operation to remove the sensing ambiguity associated with both TOs and CFOs. Without sensing ambiguity, both actual propagation delay and actual Doppler frequency of multiple targets can be obtained using CACC outputs. To exploit the redundancy of the CACC outputs and reduce the complexity, we then propose a novel mirrored-MUSIC algorithm, which halves the number of unknown parameters to be estimated, for obtaining actual values of delays and Doppler frequencies. Finally, we propose a high-resolution angles-of-arrival (AoAs) estimation algorithm, which jointly processes all measurements from spatial, temporal, and frequency domains. The proposed AoAs estimation algorithm can achieve significantly higher estimation accuracy than that of using samples from the spatial domain only. We also derive the theoretical mean-square-error of the proposed algorithms. Numerical results are provided and validate the effectiveness of the proposed scheme.