User-Echo Association and Perspective Selection with High-Resolution Range Profile for Integrated Sensing and Communication Networks

With the evolution of wireless networks towards integrated sensing and communication (ISAC) capabilities, cooperation among base stations (BSs) becomes critical for achieving precise sensing and reconstruction of user equipments (UEs). In this paper, we focus on a multi-static sensing setup, where multiple BSs aim to communicate with multiuser while determining their locations and scatterer pattern cooperatively. However, a key challenge arises from the absence of UE identities in the echoes, which makes it difficult for BSs to associate UEs with their corresponding echoes, leading to degraded communication and sensing performance. To address this issue, we propose a two-phase association algorithm based on the high-resolution range profile (HRRP) of each UE. During the first phase, to simultaneously achieve parameter estimation and initial association, we design a parallel multiuser Turbo-sparse Bayesian learning (PMT-SBL) algorithm. Since HRRP is highly sensitive to observation perspectives, we incorporate the transceiver bistatic angle into the scatterer model and establish initial associations by exploiting correlations between HRRPs. Then, the variational Bayesian inference (VBI)-expectation maximization (EM) methodology is employed to estimate unknown variables. During the second phase, we refine the association results by combining the estimated parameters and select optimal perspectives to maximize perspective diversity gain. For a more accurate reconstruction of the UE scatterer pattern, we propose the HRRP Kullback-Leibler divergence (HRRP-KLD), which effectively quantifies the structural divergence of UEs across different perspectives. Simulation results demonstrate that the proposed scheme not only achieves superior estimation and communication performance but also offers high reconstruction accuracy.