6 citations · 11 across the 26 of their papers we have counts for
9 papers · 1 filter
Distributed Attacks over Federated Reinforcement Learning-enabled Cell Sleep Control
Han Zhang, Hao Zhou, Medhat Elsayed +4
Federated learning (FL) is particularly useful in wireless networks due to its distributed implementation and privacy-preserving features. However, as a distributed learning system…
Intent-driven Intelligent Control and Orchestration in O-RAN Via Hierarchical Reinforcement Learning
Md Arafat Habib, Hao Zhou, Pedro Enrique Iturria-Rivera +5
rApps and xApps need to be controlled and orchestrated well in the open radio access network (O-RAN) so that they can deliver a guaranteed network performance in a complex multi-ve…
Cooperative Hierarchical Deep Reinforcement Learning based Joint Sleep and Power Control in RIS-aided Energy-Efficient RAN
Hao Zhou, Medhat Elsayed, Majid Bavand +3
Energy efficiency (EE) is one of the most important metrics for envisioned 6G networks, and sleep control, as a cost-efficient approach, can significantly lower power consumption b…
On-Device Intelligence for 5G RAN: Knowledge Transfer and Federated Learning enabled UE-Centric Traffic Steering
Han Zhang, Hao Zhou, Medhat Elsayed +4
Traffic steering (TS) is a promising approach to support various service requirements and enhance transmission reliability by distributing network traffic loads to appropriate base…
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 Based Traffic Steering in Multi-RAT 5G Deployments
Md Arafat Habib, Hao Zhou, Pedro Enrique Iturria-Rivera +5
In 5G non-standalone mode, an intelligent traffic steering mechanism can vastly aid in ensuring smooth user experience by selecting the best radio access technology (RAT) from a mu…
Hierarchical Deep Q-Learning Based Handover in Wireless Networks with Dual Connectivity
Pedro Enrique Iturria Rivera, Medhat Elsayed, Majid Bavand +3
5G New Radio proposes the usage of frequencies above 10 GHz to speed up LTE's existent maximum data rates. However, the effective size of 5G antennas and consequently its repercuss…
Traffic Steering for 5G Multi-RAT Deployments using Deep Reinforcement Learning
Md Arafat Habib, Hao Zhou, Pedro Enrique Iturria Rivera +5
In 5G non-standalone mode, traffic steering is a critical technique to take full advantage of 5G new radio while optimizing dual connectivity of 5G and LTE networks in multiple rad…
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…