SAGE-HB: Swift Adaptation and Generalization in Massive MIMO Hybrid Beamforming
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912230632587264 |
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| author | Karkan, Ali Hasanzadeh Hojatian, Hamed Frigon, Jean-François Leduc-Primeau, François |
| author_facet | Karkan, Ali Hasanzadeh Hojatian, Hamed Frigon, Jean-François Leduc-Primeau, François |
| contents | Deep learning (DL)-based solutions have emerged as promising candidates for beamforming in massive Multiple-Input Multiple-Output (mMIMO) systems. Nevertheless, it remains challenging to seamlessly adapt these solutions to practical deployment scenarios, typically necessitating extensive data for fine-tuning while grappling with domain adaptation and generalization issues. In response, we propose a novel approach combining Meta-Learning Domain Generalization (MLDG) with novel data augmentation techniques during fine-tuning. This approach not only accelerates adaptation to new channel environments but also significantly reduces the data requirements for fine-tuning, thereby enhancing the practicality and efficiency of DL-based mMIMO systems. The proposed approach is validated by simulating the performance of a backbone model when deployed in a new channel environment, and with different antenna configurations, path loss, and base station height parameters. Our proposed approach demonstrates superior zero-shot performance compared to existing methods and also achieves near-optimal performance with significantly fewer fine-tuning data samples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_10513 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SAGE-HB: Swift Adaptation and Generalization in Massive MIMO Hybrid Beamforming Karkan, Ali Hasanzadeh Hojatian, Hamed Frigon, Jean-François Leduc-Primeau, François Signal Processing Deep learning (DL)-based solutions have emerged as promising candidates for beamforming in massive Multiple-Input Multiple-Output (mMIMO) systems. Nevertheless, it remains challenging to seamlessly adapt these solutions to practical deployment scenarios, typically necessitating extensive data for fine-tuning while grappling with domain adaptation and generalization issues. In response, we propose a novel approach combining Meta-Learning Domain Generalization (MLDG) with novel data augmentation techniques during fine-tuning. This approach not only accelerates adaptation to new channel environments but also significantly reduces the data requirements for fine-tuning, thereby enhancing the practicality and efficiency of DL-based mMIMO systems. The proposed approach is validated by simulating the performance of a backbone model when deployed in a new channel environment, and with different antenna configurations, path loss, and base station height parameters. Our proposed approach demonstrates superior zero-shot performance compared to existing methods and also achieves near-optimal performance with significantly fewer fine-tuning data samples. |
| title | SAGE-HB: Swift Adaptation and Generalization in Massive MIMO Hybrid Beamforming |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2401.10513 |