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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2410.08318 |
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| _version_ | 1866913540818862080 |
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| author | Zhang, Mianyi Cai, Yunlong Xu, Jiaqi Swindlehurst, A. Lee |
| author_facet | Zhang, Mianyi Cai, Yunlong Xu, Jiaqi Swindlehurst, A. Lee |
| contents | Extremely large-scale arrays (XL-arrays) and ultra-high frequencies are two key technologies for sixth-generation (6G) networks, offering higher system capacity and expanded bandwidth resources. To effectively combine these technologies, it is necessary to consider the near-field spherical-wave propagation model, rather than the traditional far-field planar-wave model. In this paper, we explore a near-field communication system comprising a base station (BS) with hybrid analog-digital beamforming and multiple mobile users. Our goal is to maximize the system's sum-rate by optimizing the near-field codebook design for hybrid precoding. To enable fast adaptation to varying user distributions, we propose a meta-learning-based framework that integrates the model-agnostic meta-learning (MAML) algorithm with a codebook learning network. Specifically, we first design a deep neural network (DNN) to learn the near-field codebook. Then, we combine the MAML algorithm with the DNN to allow rapid adaptation to different channel conditions by leveraging a well-initialized model from the outer network. Simulation results demonstrate that our proposed framework outperforms conventional algorithms, offering improved generalization and better overall performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_08318 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Meta-Learning-Driven Adaptive Codebook Design for Near-Field Communications Zhang, Mianyi Cai, Yunlong Xu, Jiaqi Swindlehurst, A. Lee Signal Processing Extremely large-scale arrays (XL-arrays) and ultra-high frequencies are two key technologies for sixth-generation (6G) networks, offering higher system capacity and expanded bandwidth resources. To effectively combine these technologies, it is necessary to consider the near-field spherical-wave propagation model, rather than the traditional far-field planar-wave model. In this paper, we explore a near-field communication system comprising a base station (BS) with hybrid analog-digital beamforming and multiple mobile users. Our goal is to maximize the system's sum-rate by optimizing the near-field codebook design for hybrid precoding. To enable fast adaptation to varying user distributions, we propose a meta-learning-based framework that integrates the model-agnostic meta-learning (MAML) algorithm with a codebook learning network. Specifically, we first design a deep neural network (DNN) to learn the near-field codebook. Then, we combine the MAML algorithm with the DNN to allow rapid adaptation to different channel conditions by leveraging a well-initialized model from the outer network. Simulation results demonstrate that our proposed framework outperforms conventional algorithms, offering improved generalization and better overall performance. |
| title | Meta-Learning-Driven Adaptive Codebook Design for Near-Field Communications |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2410.08318 |