Deep Contextual Bandits for Fast Neighbor-Aided Initial Access in mmWave Cell-Free Networks
Access points (APs) in millimeter-wave (mmWave) user-centric (UC) networks will have sleep mode functionality. Initial access (IA) is a challenging problem in […]
Cooperative Edge Caching via Federated Deep Reinforcement Learning in Fog-RANs
In this paper, cooperative edge caching problem is investigated in fog radio access networks (F-RANs). By considering the non-deterministic polynomial hard (NP-hard) […]
Intelligent Resource Slicing for eMBB and URLLC Coexistence in 5G and Beyond
In this paper, we study the resource slicing problem in a dynamic multiplexing scenario of two distinct 5G services, namely Ultra-Reliable Low […]
Attention-Weighted Federated Deep Reinforcement Learning for Device-to-Device Assisted Heterogeneous Collaborative Edge Caching
In order to meet the growing demands for multimedia service access and release the pressure of the core network, edge caching and […]
Resource Awareness in Unmanned Aerial Vehicle-Assisted Mobile-Edge Computing Systems
This paper investigates an unmanned aerial vehicle (UAV)-assisted mobile-edge computing (MEC) system, in which the UAV provides complementary computation resource to the […]
Age of Information-Aware Radio Resource Management in Vehicular Networks
In this paper, we investigate the problem of age of information (AoI)-aware radio resource management for expected long-term performance optimization in a […]
Multi-Tenant Cross-Slice Resource Orchestration: A Deep Reinforcement Learning Approach
With the cellular networks becoming increasingly agile, a major challenge lies in how to support diverse services for mobile users (MUs) over […]
Optimized Computation Offloading Performance in Virtual Edge Computing Systems via Deep Reinforcement Learning
To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is a promising paradigm by providing computing capabilities in […]