Networked Sensing with AI-Empowered Environment Estimation: Exploiting Macro-Diversity and Array Gain in Perceptive Mobile Networks

Sensing will be an important service for future wireless networks to assist innovative applications like autonomous driving and environment monitoring. This paper considers the design of perceptive mobile networks (PMNs) where target monitoring terminals (TMTs) are deployed over the traditional cellular networks for jointly sensing the targets in the presence of environment clutter. Different from traditional radar, the cellular structure of PMNs offers multiple perspectives for target sensing (TS), but the joint processing among distributed sensing nodes also causes heavy computation and communication workload over the network. In this paper, we first propose a two-stage protocol where communication signals are utilized for environment estimation (EE) and TS in two consecutive time periods, respectively. A \textit{networked} sensing detector is then derived to exploit the perspectives provided by multiple TMTs for sensing the same target. The macro-diversity from multiple TMTs and the array gain from multiple receive antennas at each TMT are analyzed to reveal the benefit of networked sensing. Furthermore, we derive the sufficient condition that one TMT's contribution to the networked sensing is positive, based on which a TMT selection algorithm is proposed. To reduce the computation burden and efficiently estimate the environment, we propose a model-driven deep-learning algorithm that utilizes partially-sampled data for EE. Simulation results confirm the benefits of networked sensing and validate the higher efficiency of the proposed EE algorithm than existing methods.