Communication-Efficient and Federated Multi-Agent Reinforcement Learning
In this paper, we consider a distributed reinforcement learning setting where agents are communicating with a central entity in a shared environment […]
Millimeter Wave Communications With an Intelligent Reflector
In this paper a novel framework is proposed to optimize the downlink multi-user communication of a millimeter wave base station which is […]
V2V Cooperative Sensing using Reinforcement Learning with Action Branching
Cooperative perception plays a vital role in extending a vehicle’s sensing range beyond its line-of-sight. However, exchanging raw sensory data under limited […]
Deep Reinforcement Based Optimization of Function Splitting in Virtualized Radio Access Networks
Virtualized Radio Access Network (vRAN) is one of the key enablers of future wireless networks as it brings the agility to the […]
Collaborative Cross System AI
The emerging industrial verticals set new challenges for 5G and beyond systems. Indeed, the heterogeneity of the underlying technologies and the challenging […]
Learning-based trajectory optimization for 5G mmWave uplink UAVs
A Connectivity-constrained based path planning for unmanned aerial vehicles (UAVs) is proposed within the coverage area of a 5G NR Base Station […]
Link-Level Throughput Maximization Using Deep Reinforcement Learning
A multi-agent deep reinforcement learning framework is proposed to address link level throughput maximization by power allocation and modulation and coding scheme […]
Reinforcement Learning Based Scheduling Algorithm for Optimizing Age of Information in Ultra Reliable Low Latency Networks
Age of Information (AoI) measures the freshness of the information at a remote location. AoI reflects the time that is elapsed since […]
Reinforcement Learning Based Vehicle-cell Association Algorithm for Highly Mobile Millimeter Wave Communication
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 […]
Decentralized Deep Reinforcement Learning for Delay-Power Tradeoff in Vehicular Communications
This paper targets at the problem of radio resource management for expected long-term delay-power tradeoff in vehicular communications. At each decision epoch, […]