14 citations · 18 across the 20 of their papers we have counts for
20 papers
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…
Reimagining RAN Automation in 6G: An Agentic AI Framework with Hierarchical Online Decision Transformer
Md Arafat Habib, Medhat Elsayed, Majid Bavand +3
In this paper, we propose an Agentic Artificial Intelligence (AI) framework for wireless networks. The framework coordinates a pool of AI agents guided by Natural Language (NL) inp…
GenAI-enabled Residual Motion Estimation for Energy-Efficient Semantic Video Communication
Shavbo Salehi, Pedro Enrique Iturria-Rivera, Medhat Elsayed +3
Semantic communication addresses the limitations of the Shannon paradigm by focusing on transmitting meaning rather than exact representations, thereby reducing unnecessary resourc…
Generative AI for Intent-Driven Network Management in 6G RAN: A Case Study on the Mamba Model
Md Arafat Habib, Medhat Elsayed, Yigit Ozcan +3
With the emergence of 6G, mobile networks are becoming increasingly heterogeneous and dynamic, necessitating advanced automation for efficient management. Intent-Driven Networks (I…
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…
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…
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…
Channel Selection for Wi-Fi 7 Multi-Link Operation via Optimistic-Weighted VDN and Parallel Transfer Reinforcement Learning
Pedro Enrique Iturria-Rivera, Marcel Chenier, Bernard Herscovici +2
Dense and unplanned IEEE 802.11 Wireless Fidelity(Wi-Fi) deployments and the continuous increase of throughput and latency stringent services for users have led to machine learning…
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…
RL meets Multi-Link Operation in IEEE 802.11be: Multi-Headed Recurrent Soft-Actor Critic-based Traffic Allocation
Pedro Enrique Iturria Rivera, Marcel Chenier, Bernard Herscovici +2
IEEE 802.11be -Extremely High Throughput-, commercially known as Wireless-Fidelity (Wi-Fi) 7 is the newest IEEE 802.11 amendment that comes to address the increasingly throughput h…
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…
Cooperate or not Cooperate: Transfer Learning with Multi-Armed Bandit for Spatial Reuse in Wi-Fi
Pedro Enrique Iturria-Rivera, Marcel Chenier, Bernard Herscovici +2
The exponential increase of wireless devices with highly demanding services such as streaming video, gaming and others has imposed several challenges to Wireless Local Area Network…
Competitive Multi-Agent Load Balancing with Adaptive Policies in Wireless Networks
Pedro Enrique Iturria Rivera, Melike Erol-Kantarci
Using Machine Learning (ML) techniques for the next generation wireless networks have shown promising results in the recent years, due to high learning and adaptation capability of…
QoS-Aware Load Balancing in Wireless Networks using Clipped Double Q-Learning
Pedro Enrique Iturria Rivera, Melike Erol-Kantarci
In recent years, long-term evolution (LTE) and 5G NR (5th Generation New Radio) technologies have showed great potential to utilize Machine Learning (ML) algorithms in optimizing t…