Cooperative Sensing and User-Echo Associations for Integrated Sensing and Communication Networks
Haiying Zhang, Shuyi Chen, Weixiao Meng, Cheng Li · 2024
Wireless networks are evolving from a communication-only network to one with integrated sensing and communication (ISAC) capabilities. In such cases, the cooperation of multiple base stations (BSs) can be exploited to achieve precise sensing for multiple user equipments (UEs). However, the identities of the UEs are not contained in the echoes, making it difficult for the sensing receiver to associate UEs with their echoes when monostatic and bistatic sensing modes coexist. This leads to a loss of cooperative gain and larger echo interference between BSs, thereby degrading communication and sensing performances. To overcome this challenge and achieve multi-directional sensing of UEs, this paper develops a novel approach for parameter estimation and user-echo association using ISAC signals under doubly dispersive channels. In particular, we establish a model for multiple BSs cooperative sensing of extended UEs utilizing the orthogonal time frequency space (OTFS) signal. Meanwhile, the bistatic angles are introduced as indicators to characterize the correlation between the physical scattering structures of the UE from different directions, simplifying the complex association process. Additionally, we design a parallel off-grid sparse Bayesian learning (SBL) algorithm to estimate unknown parameters iteratively. Simulation results demonstrate that the proposed scheme achieves better NMSE and BER performance.