17 papers
AngularWM: Wireless World Modeling for AoA Prediction from Multi-Antenna I/Q
Omar Mashaal, Hatem Abou-Zeid
Tracking angle-of-arrival (AoA) under mobility enables proactive beam alignment in multi-antenna wireless systems. However, learning this evolution directly from raw I/Q data is ch…
RRM-GPT: A Framework and Vision for Radio Resource Management Foundation Models
Ahmed Aboulfotouh, Akram Bin Sediq, Koosha Pourtahmasi Roshandeh +4
Learning-based models for radio resource management (RRM) are typically built for a single function and deployment, so each new setting repeats the development pipeline. RRM decisi…
Scaling of Wireless Foundation Models via Representation Diversity and Multi-Branch Architectures
Ahmed Mohamed, Ahmed Aboulfotouh, Hatem Abou-Zeid
Wireless foundation models learn representations from unlabeled radio signals for reuse across downstream tasks. Scaling model capacity is a common strategy for learning richer rep…
VitalFR3: Wireless Vital Sign Sensing in the Upper Mid-Band (FR3)
Gholamreza Bakhshi, Hatem Abou-Zeid
Frequency range 3 (FR3) is being explored for sixth-generation (6G) integrated sensing and communication (ISAC).However, experimental evidence on the feasibility of physiological s…
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…
Fast Wireless Foundation Models with Early-Exits
Omar Mashaal, Hatem Abou-Zeid
While wireless foundation models (FMs) are demonstrating strong potential to enable AI-Native 6G networks, their high computational cost remains a critical barrier to deployment. T…
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…
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…
BEACON: Benefit-Aware Early-Exit for Automatic Modulation Classification via Recoverability Prediction
Zheng Liu, Hatem Abou-Zeid, Huaqing Wu
Convolutional neural networks (CNNs) have emerged as a powerful tool for automatic modulation classification (AMC) by directly extracting discriminative features from raw in-phase…
WirelessJEPA: A Multi-Antenna Foundation Model using Spatio-temporal Wireless Latent Predictions
Viet Chu, Omar Mashaal, Hatem Abou-Zeid
We propose WirelessJEPA, a novel wireless foundation model (WFM) that uses the Joint Embedding Predictive Architecture (JEPA). WirelessJEPA learns general-purpose representations d…
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…
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…
IQFM A Wireless Foundational Model for I/Q Streams in AI-Native 6G
Omar Mashaal, Hatem Abou-Zeid
Foundational models have shown remarkable potential in natural language processing and computer vision, yet remain in their infancy in wireless communications. While a few efforts…
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…
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…