Generative Learning Powered Probing Beam Optimization for Cell-Free Hybrid Beamforming
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866913509661474816 |
|---|---|
| author | Zhang, Cheng Xiong, Shuangbo He, Mengqing Wei, Lan Huang, Yongming Zhang, Wei |
| author_facet | Zhang, Cheng Xiong, Shuangbo He, Mengqing Wei, Lan Huang, Yongming Zhang, Wei |
| contents | Probing beam measurement (PBM)-based hybrid beamforming provides a feasible solution for cell-free MIMO. In this letter, we propose a novel probing beam optimization framework where three collaborative modules respectively realize PBM augmentation, sum-rate prediction and probing beam optimization. Specifically, the PBM augmentation model integrates the conditional variational auto-encoder (CVAE) and mixture density networks and adopts correlated PBM distribution with full-covariance, for which a Cholesky-decomposition based training is introduced to address the issues of covariance legality and numerical stability. Simulations verify the better performance of the proposed augmentation model compared to the traditional CVAE and the efficiency of proposed optimization framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_13286 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generative Learning Powered Probing Beam Optimization for Cell-Free Hybrid Beamforming Zhang, Cheng Xiong, Shuangbo He, Mengqing Wei, Lan Huang, Yongming Zhang, Wei Information Theory Signal Processing Probing beam measurement (PBM)-based hybrid beamforming provides a feasible solution for cell-free MIMO. In this letter, we propose a novel probing beam optimization framework where three collaborative modules respectively realize PBM augmentation, sum-rate prediction and probing beam optimization. Specifically, the PBM augmentation model integrates the conditional variational auto-encoder (CVAE) and mixture density networks and adopts correlated PBM distribution with full-covariance, for which a Cholesky-decomposition based training is introduced to address the issues of covariance legality and numerical stability. Simulations verify the better performance of the proposed augmentation model compared to the traditional CVAE and the efficiency of proposed optimization framework. |
| title | Generative Learning Powered Probing Beam Optimization for Cell-Free Hybrid Beamforming |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2409.13286 |