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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.07715 |
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| _version_ | 1866909249765900288 |
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| author | Peng, Ronghua Gao, Peng You, Jing Lian, Lixiang |
| author_facet | Peng, Ronghua Gao, Peng You, Jing Lian, Lixiang |
| contents | As a promising technique, reconfigurable intelligent surfaces (RISs) exhibit its tremendous potential for high accuracy positioning. In this paper, we investigates multi-user localization and tracking problem in multi-RISs-assisted system. In particular, we incorporate statistical spatiotemporal correlation of multi-user locations and develop a general spatiotemporal Markov random field model (ST-+MRF) to capture multi-user dynamic motion states. To achieve superior performance, a novel multi-user tracking algorithm is proposed based on Bayesian inference to effectively utilize the correlation among users. Besides that, considering the necessity of RISs configuration for tracking performance, we further propose a predictive RISs beamforming optimization scheme via semidefinite relaxation (SDR). Compared to other pioneering work, finally, we confirm that the proposed strategy by alternating tracking algorithm and RISs optimization, can achieve significant performance gains over benchmark schemes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_07715 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-User Localization and Tracking with Spatiotemporal Correlation in Multi-RIS-Assisted Systems Peng, Ronghua Gao, Peng You, Jing Lian, Lixiang Information Theory Signal Processing As a promising technique, reconfigurable intelligent surfaces (RISs) exhibit its tremendous potential for high accuracy positioning. In this paper, we investigates multi-user localization and tracking problem in multi-RISs-assisted system. In particular, we incorporate statistical spatiotemporal correlation of multi-user locations and develop a general spatiotemporal Markov random field model (ST-+MRF) to capture multi-user dynamic motion states. To achieve superior performance, a novel multi-user tracking algorithm is proposed based on Bayesian inference to effectively utilize the correlation among users. Besides that, considering the necessity of RISs configuration for tracking performance, we further propose a predictive RISs beamforming optimization scheme via semidefinite relaxation (SDR). Compared to other pioneering work, finally, we confirm that the proposed strategy by alternating tracking algorithm and RISs optimization, can achieve significant performance gains over benchmark schemes. |
| title | Multi-User Localization and Tracking with Spatiotemporal Correlation in Multi-RIS-Assisted Systems |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2407.07715 |