6 papers
Wireless Foundation Models: State-of-the-Art and Open Challenges
Alonso M. Pacheco Huachaca, Juan J. Rodriguez Rodriguez, Ahmed Aboulfotouh +3
Wireless foundation models (WFMs) have emerged as a promising approach for learning reusable representations from large-scale wireless data and adapting them to downstream tasks. H…
LatentWave: JEPA Pretraining for Wireless Foundation Models
Ahmed Mohamed, Ahmed Aboulfotouh, Hatem Abou-Zeid
Wireless foundation models have emerged as a promising alternative to building separate models for each wireless task. However, existing approaches rely on masked input reconstruct…
Multimodal Wireless Foundation Models
Ahmed Aboulfotouh, Hatem Abou-Zeid
Wireless foundation models (WFMs) have recently demonstrated promising capabilities, jointly performing multiple wireless functions and adapting effectively to new environments. Ho…
6G WavesFM: A Foundation Model for Sensing, Communication, and Localization
Ahmed Aboulfotouh, Elsayed Mohammed, Hatem Abou-Zeid
This paper introduces WavesFM, a novel Wireless Foundation Model (WFM) framework, capable of supporting a wide array of communication, sensing, and localization tasks. Our proposed…
Building 6G Radio Foundation Models with Transformer Architectures
Ahmed Aboulfotouh, Ashkan Eshaghbeigi, Hatem Abou-Zeid
Foundation deep learning (DL) models are general models, designed to learn general, robust and adaptable representations of their target modality, enabling finetuning across a rang…
Self-Supervised Radio Pre-training: Toward Foundational Models for Spectrogram Learning
Ahmed Aboulfotouh, Ashkan Eshaghbeigi, Dimitrios Karslidis +1
Foundational deep learning (DL) models are general models, trained on large, diverse, and unlabelled datasets, typically using self-supervised learning techniques have led to signi…