Joint Near Field Uplink Communication and Localization Using Message Passing-Based Sparse Bayesian Learning
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866913314675621888 |
|---|---|
| author | Liu, Fei Yuan, Zhengdao Guo, Qinghua Zhang, Yuanyuan Wang, Zhongyong Zhang, J. Andrew |
| author_facet | Liu, Fei Yuan, Zhengdao Guo, Qinghua Zhang, Yuanyuan Wang, Zhongyong Zhang, J. Andrew |
| contents | This work deals with the problem of uplink communication and localization in an integrated sensing and communication system, where users are in the near field (NF) of antenna aperture due to the use of high carrier frequency and large antenna arrays at base stations. We formulate joint NF signal detection and localization as a problem of recovering signals with a sparse pattern. To solve the problem, we develop a message passing based sparse Bayesian learning (SBL) algorithm, where multiple unitary approximate message passing (UAMP)-based sparse signal estimators work jointly to recover the sparse signals with low complexity. Simulation results demonstrate the effectiveness of the proposed method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_09201 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Joint Near Field Uplink Communication and Localization Using Message Passing-Based Sparse Bayesian Learning Liu, Fei Yuan, Zhengdao Guo, Qinghua Zhang, Yuanyuan Wang, Zhongyong Zhang, J. Andrew Information Theory This work deals with the problem of uplink communication and localization in an integrated sensing and communication system, where users are in the near field (NF) of antenna aperture due to the use of high carrier frequency and large antenna arrays at base stations. We formulate joint NF signal detection and localization as a problem of recovering signals with a sparse pattern. To solve the problem, we develop a message passing based sparse Bayesian learning (SBL) algorithm, where multiple unitary approximate message passing (UAMP)-based sparse signal estimators work jointly to recover the sparse signals with low complexity. Simulation results demonstrate the effectiveness of the proposed method. |
| title | Joint Near Field Uplink Communication and Localization Using Message Passing-Based Sparse Bayesian Learning |
| topic | Information Theory |
| url | https://arxiv.org/abs/2404.09201 |