Fundamental Limits and Base Station Selection for Collaborative Sensing in Perceptive Mobile Networks

Accurate localization is essential for future wireless networks and perceptive mobile networks (PMNs) were proposed as a special type of integrated sensing and communication (ISAC) scheme, where the sensing functionality is integrated into current mobile networks. One exciting opportunity for PMNs is the collaborative sensing, where the perspectives of multiple base stations (BSs) are jointly utilized to sense the same target in a more effective way. In this paper, we will investigate the fundamental limits of collaborative sensing, which is not yet available in the literature. To this end, we derive the Cramér-Rao Bound (CRB) for the mean squared error (MSE) performance of the maximum likelihood estimator. Based on the evaluation results, we further investigate the effect of several important system parameters including the number of BSs, the number of antennas at each BS, and the relative location of the target and the BSs. The results are then utilized to propose a BS selection algorithm for achieving a good trade-off between localization accuracy and system complexity. Simulation results validate the accuracy of the performance evaluation results and illustrate the effectiveness of the proposed BS selection algorithm.