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

TL;DR

This paper develops a stochastic geometry-based framework for optimizing integrated sensing and communication networks, balancing network-level sensing and communication performance through cooperative BS coordination and interference management.

Contribution

It introduces a tractable model for ISAC networks, jointly optimizes BS cluster sizes and resource allocation, and reveals key insights into interference nulling and resource tradeoffs.

Findings

Interference nulling improves average data and radar rates.

Optimal resource allocation varies for communication and sensing objectives.

The ratio of users to transmit antennas is constant at optimal performance.

Abstract

In this work, we study integrated sensing and communication (ISAC) networks with the aim of effectively balancing sensing and communication (S&C) performance at the network level. Focusing on monostatic sensing, the tool of stochastic geometry is exploited to capture the S&C performance, which facilitates us to illuminate key cooperative dependencies in the ISAC network and optimize key network-level parameters. Based on the derived tractable expression of area spectral efficiency (ASE), we formulate the optimization problem to maximize the network performance from the view point of two joint S&C metrics. Towards this end, we further jointly optimize the cooperative BS cluster sizes for S&C and the serving/probing numbers of users/targets to achieve a flexible tradeoff between S&C at the network level. It is verified that interference nulling can effectively improve the average data rate and radar information rate. Surprisingly, the optimal communication tradeoff for the case of the ASE maximization tends to employ all spacial resources towards multiplexing and diversity gain, without interference nulling. By contrast, for the sensing objectives, resource allocation tends to eliminate certain interference especially when the antenna resources are sufficient, because the inter-cell interference becomes a more dominant factor affecting sensing performance. Furthermore, we prove that the ratio of the optimal number of users and the number of transmit antennas is a constant value when the communication performance is optimal. Simulation results demonstrate that the proposed cooperative ISAC scheme achieves a substantial gain in S&C performance at the network level.