Energy-efficient Beamforming for RISs-aided Communications: Gradient Based Meta Learning
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2023
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866917754507886592 |
|---|---|
| author | Wang, Xinquan Zhu, Fenghao Zhou, Qianyun Yu, Qihao Huang, Chongwen Alhammadi, Ahmed Zhang, Zhaoyang Yuen, Chau Debbah, Mérouane |
| author_facet | Wang, Xinquan Zhu, Fenghao Zhou, Qianyun Yu, Qihao Huang, Chongwen Alhammadi, Ahmed Zhang, Zhaoyang Yuen, Chau Debbah, Mérouane |
| contents | Reconfigurable intelligent surfaces (RISs) have become a promising technology to meet the requirements of energy efficiency and scalability in future six-generation (6G) communications. However, a significant challenge in RISs-aided communications is the joint optimization of active and passive beamforming at base stations (BSs) and RISs respectively. Specifically, the main difficulty is attributed to the highly non-convex optimization space of beamforming matrices at both BSs and RISs, as well as the diversity and mobility of communication scenarios. To address this, we present a greenly gradient based meta learning beamforming (GMLB) approach. Unlike traditional deep learning based methods which take channel information directly as input, GMLB feeds the gradient of sum rate into neural networks. Coherently, we design a differential regulator to address the phase shift optimization of RISs. Moreover, we use the meta learning to iteratively optimize the beamforming matrices of BSs and RISs. These techniques make the proposed method to work well without requiring energy-consuming pre-training. Simulations show that GMLB could achieve higher sum rate than that of typical alternating optimization algorithms with the energy consumption by two orders of magnitude less. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_06861 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Energy-efficient Beamforming for RISs-aided Communications: Gradient Based Meta Learning Wang, Xinquan Zhu, Fenghao Zhou, Qianyun Yu, Qihao Huang, Chongwen Alhammadi, Ahmed Zhang, Zhaoyang Yuen, Chau Debbah, Mérouane Information Theory Signal Processing Reconfigurable intelligent surfaces (RISs) have become a promising technology to meet the requirements of energy efficiency and scalability in future six-generation (6G) communications. However, a significant challenge in RISs-aided communications is the joint optimization of active and passive beamforming at base stations (BSs) and RISs respectively. Specifically, the main difficulty is attributed to the highly non-convex optimization space of beamforming matrices at both BSs and RISs, as well as the diversity and mobility of communication scenarios. To address this, we present a greenly gradient based meta learning beamforming (GMLB) approach. Unlike traditional deep learning based methods which take channel information directly as input, GMLB feeds the gradient of sum rate into neural networks. Coherently, we design a differential regulator to address the phase shift optimization of RISs. Moreover, we use the meta learning to iteratively optimize the beamforming matrices of BSs and RISs. These techniques make the proposed method to work well without requiring energy-consuming pre-training. Simulations show that GMLB could achieve higher sum rate than that of typical alternating optimization algorithms with the energy consumption by two orders of magnitude less. |
| title | Energy-efficient Beamforming for RISs-aided Communications: Gradient Based Meta Learning |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2311.06861 |