Networked Sensing With AI-Empowered Interference Management: Exploiting Macro-Diversity and Array Gain in Perceptive Mobile Networks

Sensing will become an important service of future wireless networks to assist innovative applications such as autonomous driving and environment monitoring. Perceptive mobile networks (PMNs) were proposed to incorporate sensing capability into current cellular networks. However, the interference management between sensing and communication, as well as the collaborative sensing by multiple sensing nodes (SNs), faces significant challenges. In this paper, we first propose a two-stage protocol to tackle the interference between two sub-systems, where the echoes created by communication signals, i.e., interference for sensing, are estimated in the clutter estimation (CE) stage and then utilized for interference management in the target sensing (TS) stage. Then, a networked sensing detector is derived to exploit the perspectives provided by multiple SNs for sensing the same target. The macro-diversity from multiple SNs, the array gain, and the higher angular resolution from multiple receive antennas of each SN are then investigated to reveal the benefit of networked sensing. Furthermore, we derive the sufficient condition for one SN’s contribution to be positive, based on which a SN selection algorithm is proposed. To reduce the communication workload, we propose a distributed model-driven deep-learning algorithm that utilizes partially-sampled data for CE. Simulation results demonstrate the benefits of networked sensing and validate the higher efficiency of the proposed CE algorithm than existing methods.