Building 6G Radio Foundation Models with Transformer Architectures

• URL: http://arxiv.org/abs/2411.09996v1
• Date: Fri, 15 Nov 2024 07:01:44 GMT
• Title: Building 6G Radio Foundation Models with Transformer Architectures
• Authors: Ahmed Aboulfotouh, Ashkan Eshaghbeigi, Hatem Abou-Zeid,
• Abstract summary: Foundation deep learning (DL) models are designed to learn general, robust and adaptable representations of their target modality. These models are pretrained on large, unlabeled datasets using self-supervised learning (SSL) We propose and demonstrate the effectiveness of a Vision Transformer (ViT) as a radio foundation model for spectrogram learning.
• Score: 6.70088826174291
• License:
• Abstract: Foundation deep learning (DL) models are general models, designed to learn general, robust and adaptable representations of their target modality, enabling finetuning across a range of downstream tasks. These models are pretrained on large, unlabeled datasets using self-supervised learning (SSL). Foundation models have demonstrated better generalization than traditional supervised approaches, a critical requirement for wireless communications where the dynamic environment demands model adaptability. In this work, we propose and demonstrate the effectiveness of a Vision Transformer (ViT) as a radio foundation model for spectrogram learning. We introduce a Masked Spectrogram Modeling (MSM) approach to pretrain the ViT in a self-supervised fashion. We evaluate the ViT-based foundation model on two downstream tasks: Channel State Information (CSI)-based Human Activity sensing and Spectrogram Segmentation. Experimental results demonstrate competitive performance to supervised training while generalizing across diverse domains. Notably, the pretrained ViT model outperforms a four-times larger model that is trained from scratch on the spectrogram segmentation task, while requiring significantly less training time, and achieves competitive performance on the CSI-based human activity sensing task. This work demonstrates the effectiveness of ViT with MSM for pretraining as a promising technique for scalable foundation model development in future 6G networks.

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