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eess.SP2026

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…

eess.SP2026

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…

eess.SP2026

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…

eess.SP2026

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…

eess.SP2025

Diffusion Models for Wireless Transceivers: From Pilot-Efficient Channel Estimation to AI-Native 6G Receivers

Yuzhi Yang, Sen Yan, Weijie Zhou +4

With the development of artificial intelligence (AI) techniques, implementing AI-based techniques to improve wireless transceivers becomes an emerging research topic. Within this c…