6 papers
DeepTelecom: A Differentiable Multimodal Framework for Channel Twinning in Dynamic Heterogeneous Wireless Environments
Bohao Wang, Tianen Yuan, William Yen +12
Large-scale multimodal data are a key enabler for integrating large generative artificial intelligence models into wireless networks. However, existing wireless datasets offer limi…
RF-Agent: Hierarchical Language-Agent Control for Instruction-Conditioned Active Spectrum Sensing
Hao Zhang, Dongfang Xu, Hang Zou +7
Active radio-frequency (RF) sensing must acquire evidence under receiver limits while following confirmation, recovery, and stopping instructions. \newhl{We propose RF-Agent, a clo…
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…
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…