1.2k citations · 9.5k across the 261 of their papers we have counts for
14 papers · 2 filters
Predictive Ultra-Reliable Communication: A Survival Analysis Perspective
Sumudu Samarakoon, Mehdi Bennis, Walid Saad +1
Ultra-reliable communication (URC) is a key enabler for supporting immersive and mission-critical 5G applications. Meeting the strict reliability requirements of these applications…
Age-Optimal Power Allocation in Industrial IoT: A Risk-Sensitive Federated Learning Approach
Yung-Lin Hsu, Chen-Feng Liu, Sumudu Samarakoon +2
This work studies a real-time environment monitoring scenario in the industrial Internet of things, where wireless sensors proactively collect environmental data and transmit it to…
Integrating LEO Satellites and Multi-UAV Reinforcement Learning for Hybrid FSO/RF Non-Terrestrial Networks
Ju-Hyung Lee, Jihong Park, Mehdi Bennis +1
A mega-constellation of low-altitude earth orbit (LEO) satellites (SATs) and burgeoning unmanned aerial vehicles (UAVs) are promising enablers for high-speed and long-distance comm…
Distributed Heteromodal Split Learning for Vision Aided mmWave Received Power Prediction
Yusuke Koda, Jihong Park, Mehdi Bennis +3
The goal of this work is the accurate prediction of millimeter-wave received power leveraging both radio frequency (RF) signals and heterogeneous visual data from multiple distribu…
Energy Efficiency Through Joint Routing and Function Placement in Different Modes of SDN/NFV Networks
Reza Moosavi, Saeedeh Parsaeefard, Mohammad Ali Maddah-Ali +3
Network function virtualization (NFV) and software defined networking (SDN) are two promising technologies to enable 5G and 6G services and achieve cost reduction, network scalabil…
Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks
Ju-Hyung Lee, Jihong Park, Mehdi Bennis +1
A mega-constellation of low-earth orbit (LEO) satellites has the potential to enable long-range communication with low latency. Integrating this with burgeoning unmanned aerial veh…
Data-Driven Predictive Scheduling in Ultra-Reliable Low-Latency Industrial IoT: A Generative Adversarial Network Approach
Chen-Feng Liu, Mehdi Bennis
To date, model-based reliable communication with low latency is of paramount importance for time-critical wireless control systems. In this work, we study the downlink (DL) control…
Deep Learning Assisted CSI Estimation for Joint URLLC and eMBB Resource Allocation
Hamza Khan, M. Majid Butt, Sumudu Samarakoon +2
Multiple-input multiple-output (MIMO) is a key for the fifth generation (5G) and beyond wireless communication systems owing to higher spectrum efficiency, spatial gains, and energ…
Communication-Efficient Multimodal Split Learning for mmWave Received Power Prediction
Yusuke Koda, Jihong Park, Mehdi Bennis +3
The goal of this study is to improve the accuracy of millimeter wave received power prediction by utilizing camera images and radio frequency (RF) signals, while gathering image in…
Intelligent Resource Slicing for eMBB and URLLC Coexistence in 5G and Beyond: A Deep Reinforcement Learning Based Approach
Madyan Alsenwi, Nguyen H. Tran, Mehdi Bennis +3
In this paper, we study the resource slicing problem in a dynamic multiplexing scenario of two distinct 5G services, namely Ultra-Reliable Low Latency Communications (URLLC) and en…
Communication-Efficient Massive UAV Online Path Control: Federated Learning Meets Mean-Field Game Theory
Hamid Shiri, Jihong Park, Mehdi Bennis
This paper investigates the control of a massive population of UAVs such as drones. The straightforward method of control of UAVs by considering the interactions among them to make…
Enhancing Video Streaming in Vehicular Networks via Resource Slicing
Hamza Khan, Sumudu Samarakoon, Mehdi Bennis
Vehicle-to-everything (V2X) communication is a key enabler that connects vehicles to neighboring vehicles, infrastructure and pedestrians. In the past few years, multimedia service…
Federated Learning under Channel Uncertainty: Joint Client Scheduling and Resource Allocation
Madhusanka Manimel Wadu, Sumudu Samarakoon, Mehdi Bennis
In this work, we propose a novel joint client scheduling and resource block (RB) allocation policy to minimize the loss of accuracy in federated learning (FL) over wireless compare…
Reinforcement Learning Based Vehicle-cell Association Algorithm for Highly Mobile Millimeter Wave Communication
Hamza Khan, Anis Elgabli, Sumudu Samarakoon +2
Vehicle-to-everything (V2X) communication is a growing area of communication with a variety of use cases. This paper investigates the problem of vehicle-cell association in millime…