تقرير
SAGE-HB: Swift Adaptation and Generalization in Massive MIMO Hybrid Beamforming
| العنوان: | SAGE-HB: Swift Adaptation and Generalization in Massive MIMO Hybrid Beamforming |
|---|---|
| المؤلفون: | Karkan, Ali Hasanzadeh, Hojatian, Hamed, Frigon, Jean-François, Leduc-Primeau, François |
| سنة النشر: | 2024 |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Signal Processing |
| الوصف: | 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. Comment: This preprint comprises 6 pages and features 5 figures. It has been accepted to the IEEE International Conference on Machine Learning and Computer Networking (ICMLCN) 2024 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2401.10513 |
| رقم الأكسشن: | edsarx.2401.10513 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |