AI-Empowered Bi-Static Sensing for Rank-Limited Communications

This letter proposes an AI-empowered localization method for passive objects using rank-limited communication signals in bi-static scenario. Specifically, we consider linearly precoded communication signals that are rank-limited by the total number of independent data streams, resulting in traditional subspace methods failing to find correct angular information. A deep neural network (DNN) based solution is proposed to accurately localize the targets under reflections from clutters and inter-cell interference. The proposed DNN model applies data fusion over multiple user equipments to overcome the limitations of using rank-limited signals. The numerical results show the efficiency of the developed technique over baselines.