collaborators

5 papers

eess.SP2026

Explainable AI for Next-Generation Wireless Physical Layer: Basics, State-of-the-Art, and Open Challenges

Bingnan Xiao, Shuyan Hu, Xiaojing Chen +5

Next-generation wireless systems are expected to be ``AI-native," with neural networks (NNs) embedded throughout the physical (PHY) layer protocol stack to improve spectral efficie…

cs.IT2026

Science-Informed Design of Deep Learning With Applications to Wireless Systems: A Tutorial

Atefeh Termehchi, Ekram Hossain, Angelo Vera-Rivera +2

Recent advances in computational infrastructure and large-scale data processing have accelerated the adoption of data-driven inference methods, particularly deep learning (DL), to…

cs.LG2026

Generalization Analysis and Method for Domain Generalization for a Family of Recurrent Neural Networks

Atefeh Termehchi, Ekram Hossain, Isaac Woungang

Deep learning (DL) has driven broad advances across scientific and engineering domains. Despite its success, DL models often exhibit limited interpretability and generalization, wh…

eess.SY2025

Beamforming Control in RIS-Aided Wireless Communications: A Predictive Physics-Based Approach

Luis C. Mathias, Atefeh Termehchi, Taufik Abrão +1

Integrating reconfigurable intelligent surfaces (RIS) into wireless communication systems is a promising approach for enhancing coverage and data rates by intelligently redirecting…

cs.LG2025

Koopman-Based Generalization of Deep Reinforcement Learning With Application to Wireless Communications

Atefeh Termehchi, Ekram Hossain, Isaac Woungang

Deep Reinforcement Learning (DRL) is a key machine learning technology driving progress across various scientific and engineering fields, including wireless communication. However,…