6 citations · 9 across the 24 of their papers we have counts for
24 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…
Reference-Based Recursive Least-Squares Mitigation of Real Interference in Stereo Audio Recordings
Necati Kagan Erkek, Y. Ugur Ozcan
Reference-based adaptive interference cancellation is evaluated for stereo audio recordings corrupted by real train noise and environmental background. The observed signal is model…
Pilot-Aided MIMO Channel Identification and Linear Deconvolution in Correlated Gaussian Noise
Necati Kagan Erkek, Y. Ugur Ozcan
This paper presents a pilot-aided study of multiple-input multiple-output (MIMO) channel identification and linear deconvolution under spatially correlated Gaussian noise. A real-v…
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…
Hierarchical Decision Mamba Meets Agentic AI: A Novel Approach for RAN Slicing in 6G
Md Arafat Habib, Medhat Elsayed, Majid Bavand +3
Radio Access Network (RAN) slicing enables multiple logical networks to exist on top of the same physical infrastructure by allocating resources to distinct service groups, where r…
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…
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…
LLM-Based Intent Processing and Network Optimization Using Attention-Based Hierarchical Reinforcement Learning
Md Arafat Habib, Pedro Enrique Iturria Rivera, Yigit Ozcan +4
Intent-based network automation is a promising tool to enable easier network management however certain challenges need to be effectively addressed. These are: 1) processing intent…
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…
Transformer-Based Wireless Traffic Prediction and Network Optimization in O-RAN
Md Arafat Habib, Pedro Enrique Iturria-Rivera, Yigit Ozcan +4
This paper introduces an innovative method for predicting wireless network traffic in concise temporal intervals for Open Radio Access Networks (O-RAN) using a transformer architec…
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…
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…
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…
Reconfigurable Intelligent Surfaces for the Connectivity of Autonomous Vehicles
Y. Ugur Ozcan, Ozgur Ozdemir, Gunes Karabulut Kurt
The use of real-time software-controlled reconfigurable intelligent surface (RIS) units is proposed to increase the reliability of vehicle-to-everything (V2X) communications. The o…