Cooperative ISAC Networks: Performance Analysis, Scaling Laws and Optimization

TL;DR

This paper analyzes the performance of cooperative ISAC networks, deriving scaling laws and optimizing network parameters to balance sensing and communication, revealing how transceiver deployment impacts overall network performance.

Contribution

It introduces a novel cooperative ISAC scheme using multi-point transmission and MIMO radar, and derives key performance scaling laws and optimization strategies.

Findings

Deploying N transceivers improves sensing performance with a ln^2N scaling law.

Performance gain is less than N^2 due to path loss from distant base stations.

A low-complexity algorithm optimizes cluster size and transmit power for balanced S&C performance.

Abstract

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.