{"ID":2823067,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.01152","arxiv_id":"2601.01152","title":"Towards a Theoretical Framework for Robust Node Deployment in Cooperative ISAC Networks","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.","short_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 metri...","url_abs":"https://arxiv.org/abs/2601.01152","url_pdf":"https://arxiv.org/pdf/2601.01152v1","authors":"[\"Haojin Li\",\"Kaiqian Qu\",\"Chen Sun\",\"Anbang Zhang\",\"Xiaoxue Wang\",\"Wenqi Zhang\",\"Haijun Zhang\"]","published":"2026-01-03T10:44:11Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[]","has_code":false}