6 citations · 11 across the 26 of their papers we have counts for
4 papers · 1 filter
Agentic Quantum Deep Reinforcement Learning for RAN Slicing
Tingnan Bao, Medhat Elsayed, Pedro Enrique Iturria-Rivera +3
Radio access network (RAN) slicing enables ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB) services to share radio resources, but their requi…
Federated Learning with Dual Attention for Robust Modulation Classification under Attacks
Han Zhang, Medhat Elsayed, Majid Bavand +3
Federated learning (FL) allows distributed participants to train machine learning models in a decentralized manner. It can be used for radio signal classification with multiple rec…
Beam Selection for Energy-Efficient mmWave Network Using Advantage Actor Critic Learning
Ycaro Dantas, Pedro Enrique Iturria-Rivera, Hao Zhou +4
The growing adoption of mmWave frequency bands to realize the full potential of 5G, turns beamforming into a key enabler for current and next-generation wireless technologies. Many…
Hierarchical Reinforcement Learning for RIS-Assisted Energy-Efficient RAN
Hao Zhou, Long Kong, Medhat Elsayed +4
Reconfigurable intelligent surface (RIS) is emerging as a promising technology to boost the energy efficiency (EE) of 5G beyond and 6G networks. Inspired by this potential, in this…