7 papers
A JEPA-Based Field-Layer World Model for Bridging Channel Prediction and Estimation
Yuzhi Yang, Brahim Mefgouda, Hang Zou +5
Channel state information (CSI) acquisition, reconstruction, and prediction are fundamental yet costly tasks in modern MIMO-OFDM wireless systems. Direct coefficient-level predicti…
LLM-Based Digital Twin Intelligence for Application-Aware Network Selection in 6G Heterogeneous Wireless Networks
Brahim Mefgouda, Anis Bara, Lina Bariah +3
Future 6G heterogeneous wireless networks (HWNs) are expected to support multiple radio access technologies (RATs), dynamic wireless environments, and applications with diverse qua…
Reading Radio from Camera: Visually-Grounded, Lightweight, and Interpretable RSSI Prediction
Sen Yan, Tianyu Hu, Brahim Mefgouda +2
Accurate, real-time wireless signal prediction is essential for next-generation networks. However, existing vision-based frameworks often rely on computationally intensive models a…
RF-Analyzer: Can Vision-Language Models Learn RF Understanding from Synthetic Data?
Anis Bara, Lina Bariah, Hang Zou +2
Understanding the wireless spectrum is a fundamen- tal requirement for intelligent communication systems, however, interpreting spectrograms requires extracting multiple physical a…
Telecom World Models: Unifying Digital Twins, Foundation Models, and Predictive Planning for 6G
Hang Zou, Yuzhi Yang, Lina Bariah +15
The integration of machine learning tools into telecom networks, has led to two prevailing paradigms, namely, language-based systems, such as Large Language Models (LLMs), and phys…
TelcoAgent-Bench: A Multilingual Benchmark for Telecom AI Agents
Lina Bariah, Brahim Mefgouda, Farbod Tavakkoli +3
The integration of large language model (LLM) agents into telecom networks introduces new challenges, related to intent recognition, tool execution, and resolution generation, whil…