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.
@article{arxiv.2601.01152,
title = {Towards a Theoretical Framework for Robust Node Deployment in Cooperative ISAC Networks},
author = {Haojin Li and Kaiqian Qu and Chen Sun and Anbang Zhang and Xiaoxue Wang and Wenqi Zhang and Haijun Zhang},
journal= {arXiv preprint arXiv:2601.01152},
year = {2026}
}