5 papers
ProtoAoA: Few-Shot Angle-of-Arrival Estimation using Prototypical Networks
Elsayed Mohammed, Omar Mashaal, Alec Digby +4
Angle-of-arrival (AoA) estimation is a crucial function in wireless communications used for localization, beam-forming, interference management, and other applications. Deep learni…
Self-supervised Radio Representation Learning: Can we Learn Multiple Tasks?
Ogechukwu Kanu, Ashkan Eshaghbeigi, Hatem Abou-Zeid
Artificial intelligence (AI) is anticipated to play a pivotal role in 6G. However, a key challenge in developing AI-powered solutions is the extensive data collection and labeling…
ProtoBeam: Generalizing Deep Beam Prediction to Unseen Antennas using Prototypical Networks
Omar Mashaal, Elsayed Mohammed, Alec Digby +3
Deep learning techniques have recently emerged to efficiently manage mmWave beam transmissions without requiring time consuming beam sweeping strategies. A fundamental challenge in…
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…