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In this work, we study integrated sensing and communication (ISAC) networkswith the aim of effectively balancing sensing and communication (S&C)performance at the network level. Focusing on monostatic sensing, the tool ofstochastic geometry is exploited to capture the S&C performance, whichfacilitates us to illuminate key cooperative dependencies in the ISAC networkand optimize key network-level parameters. Based on the derived tractableexpression of area spectral efficiency (ASE), we formulate the optimizationproblem to maximize the network performance from the view point of two jointS&C metrics. Towards this end, we further jointly optimize the cooperative BScluster sizes for S&C and the serving/probing numbers of users/targets toachieve a flexible tradeoff between S&C at the network level. It is verifiedthat interference nulling can effectively improve the average data rate andradar information rate. Surprisingly, the optimal communication tradeoff forthe case of the ASE maximization tends to employ all spacial resources towardsmultiplexing and diversity gain, without interference nulling. By contrast, forthe sensing objectives, resource allocation tends to eliminate certaininterference especially when the antenna resources are sufficient, because theinter-cell interference becomes a more dominant factor affecting sensingperformance. Furthermore, we prove that the ratio of the optimal number ofusers and the number of transmit antennas is a constant value when thecommunication performance is optimal. Simulation results demonstrate that theproposed cooperative ISAC scheme achieves a substantial gain in S&C performanceat the network level.

Network-Level Integrated Sensing and Communication: Interference Management and BS Coordination Using Stochastic Geometry