Networked Sensing with AI-Empowered Environment Estimation: Exploiting Macro-Diversity and Array Gain in Perceptive Mobile Networks
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Sensing will be an important service for future wireless networks to assist innovative applications like autonomous driving and environment monitoring. As a specific type of integrated sensing and communication (ISAC) system, perceptive mobile networks (PMNs) were proposed to add sensing capability to current cellular networks. Different from traditional radar, the cellular structure of PMNs offers multiple perspectives to sense the same target, but the joint processing among distributed sensing nodes (SNs) 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 target sensing (TS) in two consecutive time periods, respectively. A networked sensing detector is then derived to exploit the perspectives provided by multiple SNs for sensing the same target. The macro-diversity from multiple SNs and the array gain from multiple receive antennas at each SN are investigated to reveal the benefit of networked sensing. Furthermore, we derive the sufficient condition that one SN's contribution to networked sensing is positive, based on which a SN selection algorithm is proposed. To reduce the computation and communication workload, 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.
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