Towards a Theoretical Framework for Robust Node Deployment in Cooperative ISAC Networks
Fuente:
arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908746593075200 |
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| author | Li, Haojin Qu, Kaiqian Sun, Chen Zhang, Anbang Wang, Xiaoxue Zhang, Wenqi Zhang, Haijun |
| author_facet | Li, Haojin Qu, Kaiqian Sun, Chen Zhang, Anbang Wang, Xiaoxue Zhang, Wenqi Zhang, Haijun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_01152 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Towards a Theoretical Framework for Robust Node Deployment in Cooperative ISAC Networks Li, Haojin Qu, Kaiqian Sun, Chen Zhang, Anbang Wang, Xiaoxue Zhang, Wenqi Zhang, Haijun Signal Processing 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. |
| title | Towards a Theoretical Framework for Robust Node Deployment in Cooperative ISAC Networks |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2601.01152 |