8 papers
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…
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…
Simulation-Driven Ensemble Machine Learning for Robust and Generalizable Path Loss Prediction
Ahmed P. Mohamed, Byunghyun Lee, Yaguang Zhang +3
Machine learning has emerged as a promising approach to path loss prediction, yet its effectiveness often degrades when measurement data are scarce. To address this limitation, we…
Simulation-Enhanced Data Augmentation for Machine Learning Pathloss Prediction
Ahmed P. Mohamed, Byunghyun Lee, Yaguang Zhang +4
Machine learning (ML) offers a promising solution to pathloss prediction. However, its effectiveness can be degraded by the limited availability of data. To alleviate these challen…
Data-Driven Subsampling in the Presence of an Adversarial Actor
Abu Shafin Mohammad Mahdee Jameel, Ahmed P. Mohamed, Jinho Yi +2
Deep learning based automatic modulation classification (AMC) has received significant attention owing to its potential applications in both military and civilian use cases. Recent…
Knowledge Distillation For Wireless Edge Learning
Ahmed P. Mohamed, Abu Shafin Mohammad Mahdee Jameel, Aly El Gamal
In this paper, we propose a framework for predicting frame errors in the collaborative spectrally congested wireless environments of the DARPA Spectrum Collaboration Challenge (SC2…
A Number Theoretic Approach for Fast Discovery of Single-Hop Wireless Networks
Tolunay Seyfi, Ahmed P. Mohamed, Aly El Gamal
Interference management has become a key factor in regulating transmissions in wireless communication networks. To support effective interference management schemes, it can be esse…
Deep Learning for Frame Error Prediction using a DARPA Spectrum Collaboration Challenge (SC2) Dataset
Abu Shafin Mohammad Mahdee Jameel, Ahmed P. Mohamed, Xiwen Zhang +1
We demonstrate a first example for employing deep learning in predicting frame errors for a Collaborative Intelligent Radio Network (CIRN) using a dataset collected during particip…