Towards a Theoretical Framework for Robust Node Deployment in Cooperative ISAC Networks

TL;DR

This paper develops a theoretical framework for optimizing node deployment in cooperative ISAC networks to improve localization robustness by minimizing steering vector correlation, using a genetic algorithm for optimization.

Contribution

It introduces a novel correlation metric and a deployment optimization framework, solved with a genetic algorithm, to enhance worst-case localization robustness in ISAC networks.

Findings

Significant improvement in localization robustness demonstrated

Effective reduction of steering vector correlation achieved

Validation through simulations with MUSIC and neural-network methods

Abstract

This paper investigates node deployment strategies for robust multi-node cooperative localization in integrated sensing and communication (ISAC) networks.We first analyze how steering vector correlation across different positions affects localization performance and introduce a novel distance-weighted correlation metric to characterize this effect. Building upon this insight, we propose a deployment optimization framework that minimizes the maximum weighted steering vector correlation by optimizing simultaneously node positions and array orientations, thereby enhancing worst-case network robustness. Then, a genetic algorithm (GA) is developed to solve this min-max optimization, yielding optimized node positions and array orientations. Extensive simulations using both multiple signal classification (MUSIC) and neural-network (NN)-based localization validate the effectiveness of the proposed methods, demonstrating significant improvements in robust localization performance.