Efficient Asynchronous Uplink Sensing in Perceptive Mobile Networks via Accurate Delay Estimation

In perceptive mobile networks (PMNs) with integrated sensing and communication (ISAC), sensing ambiguity in ranging and velocity measurements can be caused by timing offsets (TOs) and carrier frequency offsets (CFOs) between asynchronous sensing transmitter and receiver. In this paper, we propose an efficient method for uplink sensing with asynchronous transceivers, via accurately estimating path delays including TOs. Firstly, we develop a sparse signal recovery model to decouple multiple sensing parameters, i.e., delay, angle of arrival (AoA) and Doppler for multipath, facilitating efficient and low-complexity sensing parameter estimation. Then, leveraging unitary approximate message passing (UAMP) and sparse Bayesian learning (SBL), we obtain initial delay and signal estimates, which are then refined through Gauss-Seidel iteration. Finally, we conduct estimation and association of the remaining sensing parameters by coherently processing cross-correlation of received signals across the spatial and temporal domains. This effectively removes the sensing ambiguity incurred by time-varying TOs and CFOs, enabling accurate estimation of range and velocity for multiple targets. Simulation results verify the effectiveness of the proposed method, demonstrating its superior sensing performance, compared to existing methods such as cross-antenna cross correlation (CACC).