6 citations · 10 across the 20 of their papers we have counts for
20 papers
Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-driven RAN Management in 6G
Md Arafat Habib, Pedro Enrique Iturria Rivera, Yigit Ozcan +4
Intent-driven network management is critical for managing the complexity of 5G and 6G networks. It enables adaptive, on-demand management of the network based on the objectives of…
Intelligent Attacks and Defense Methods in Federated Learning-enabled Energy-Efficient Wireless Networks
Han Zhang, Hao Zhou, Medhat Elsayed +4
Federated learning (FL) is a promising technique for learning-based functions in wireless networks, thanks to its distributed implementation capability. On the other hand, distribu…
Prioritized Value-Decomposition Network for Explainable AI-Enabled Network Slicing
Shavbo Salehi, Pedro Enrique Iturria-Rivera, Medhat Elsayed +4
Network slicing aims to enhance flexibility and efficiency in next-generation wireless networks by allocating the right resources to meet the diverse requirements of various applic…
Explainable Multi-Agent Reinforcement Learning for Extended Reality Codec Adaptation
Pedro Enrique Iturria-Rivera, Raimundas Gaigalas, Medhat Elsayed +3
Extended Reality (XR) services are set to transform applications over 5th and 6th generation wireless networks, delivering immersive experiences. Concurrently, Artificial Intellige…
Cooperation and Personalization on a Seesaw: Choice-based FL for Safe Cooperation in Wireless Networks
Han Zhang, Medhat Elsayed, Majid Bavand +3
Federated learning (FL) is an innovative distributed artificial intelligence (AI) technique. It has been used for interdisciplinary studies in different fields such as healthcare,…
Smart Jamming Attack and Mitigation on Deep Transfer Reinforcement Learning Enabled Resource Allocation for Network Slicing
Shavbo Salehi, Hao Zhou, Medhat Elsayed +4
Network slicing is a pivotal paradigm in wireless networks enabling customized services to users and applications. Yet, intelligent jamming attacks threaten the performance of netw…
Machine Learning-enabled Traffic Steering in O-RAN: A Case Study on Hierarchical Learning Approach
Md Arafat Habib, Hao Zhou, Pedro Enrique Iturria-Rivera +5
Traffic Steering is a crucial technology for wireless networks, and multiple efforts have been put into developing efficient Machine Learning (ML)-enabled traffic steering schemes…
Self-Play Ensemble Q-learning enabled Resource Allocation for Network Slicing
Shavbo Salehi, Pedro Enrique Iturria-Rivera, Medhat Elsayed +4
In 5G networks, network slicing has emerged as a pivotal paradigm to address diverse user demands and service requirements. To meet the requirements, reinforcement learning (RL) al…
Extended Reality (XR) Codec Adaptation in 5G using Multi-Agent Reinforcement Learning with Attention Action Selection
Pedro Enrique Iturria-Rivera, Raimundas Gaigalas, Medhat Elsayed +3
Extended Reality (XR) services will revolutionize applications over 5th and 6th generation wireless networks by providing seamless virtual and augmented reality experiences. These…
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…
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…
Design Principles for Model Generalization and Scalable AI Integration in Radio Access Networks
Pablo Soldati, Euhanna Ghadimi, Burak Demirel +3
Artificial intelligence (AI) has emerged as a powerful tool for addressing complex and dynamic tasks in radio communication systems. Research in this area, however, focused on AI s…
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…