Integrated sensing and communication (ISAC) networks are investigated with the objective of effectively balancing the sensing and communication (S&C) performance at the network level. Through the simultaneous utilization of multi-point (CoMP) coordinated joint transmission and distributed multiple-input multiple-output (MIMO) radar techniques, we propose an innovative networked ISAC scheme, where multiple transceivers are employed for collaboratively enhancing the S&C services. Then, the potent tool of stochastic geometry is exploited for characterizing the S&C performance, which allows us to illuminate the key cooperative dependencies in the ISAC network and optimize salient network-level parameters. Remarkably, the Cramer-Rao lower bound (CRLB) expression of the localization accuracy derived unveils a significant finding: Deploying N ISAC transceivers yields an enhanced average cooperative sensing performance across the entire network, in accordance with the ln^2N scaling law. Crucially, this scaling law is less pronounced in comparison to the performance enhancement of N^2 achieved when the transceivers are equidistant from the target, which is primarily due to the substantial path loss from the distant base stations (BSs) and leads to reduced contributions to sensing performance gain. Moreover, we derive a tight expression of the communication rate, and present a low-complexity algorithm to determine the optimal cooperative cluster size. Based on our expression derived for the S&C performance, we formulate the optimization problem of maximizing the network performance in terms of two joint S&C metrics. To this end, we jointly optimize the cooperative BS cluster sizes and the transmit power to strike a flexible tradeoff between the S&C performance.

翻译：本文研究集成感知与通信网络，旨在网络层面有效平衡感知与通信性能。通过协同利用多点协作联合传输与分布式多输入多输出雷达技术，我们提出一种创新的网络化ISAC方案，其中多个收发器协同增强感知与通信服务。随后，利用随机几何这一有力工具刻画感知与通信性能，从而揭示ISAC网络中的关键协作依赖关系并优化核心网络级参数。值得注意的是，所推导的定位精度克拉美-罗下界表达式揭示了一个重要发现：部署N个ISAC收发器可依据ln^2N缩放定律提升全网平均协作感知性能。关键在于，该缩放定律相较于收发器与目标等距时所能实现的N^2性能增强更为平缓，这主要源于远端基站的大尺度路径损耗导致其对感知性能增益的贡献减弱。此外，我们推导了通信速率的紧致表达式，并提出一种低复杂度算法以确定最优协作集群规模。基于所建立的感知与通信性能表达式，我们构建了以两项联合感知-通信指标最大化网络性能的优化问题，通过联合优化协作基站集群规模与发射功率，实现感知与通信性能间的灵活权衡。