Double Deep Q-Learning-based Path Selection and Service Placement for Latency-Sensitive Beyond 5G Applications
Nowadays, as the need for capacity continues to grow, entirely novel services are emerging. A solid cloud-network integrated infrastructure is necessary to […]
Joint Service Migration and Resource Allocation in Edge IoT System Based on Deep Reinforcement Learning
Multi-access Edge Computing (MEC) provides services for resource-sensitive and delay-sensitive Internet of Things (IoT) applications by extending the capabilities of cloud computing […]
Optimal Traffic Load Allocation for Aloha-Based IoT LEO Constellations
The deployment of satellite networks is key to providing global wireless connectivity for the Internet of Things (IoT). In this line, we […]
Traffic Prediction and Fast Uplink for Hidden Markov IoT Models
In this work we present a novel traffic prediction and fast uplink (FU) framework for IoT networks controlled by binary Markovian events. […]
Time-Triggered Federated Learning Over Wireless Networks
The newly emerging federated learning (FL) framework offers a new way to train machine learning models in a privacy-preserving manner. However traditional […]
Energy Efficiency Maximization in the Uplink Delta-OMA Networks
Delta-orthogonal multiple access (D-OMA) has been recently investigated as a potential technique to enhance the spectral efficiency in the sixth-generation (6G) networks. […]
Joint Client Scheduling and Resource Allocation Under Channel Uncertainty in Federated Learning
The performance of federated learning (FL) over wireless networks depend on the reliability of the client-server connectivity and clients’ local computation capabilities. […]
Deep learning assisted CSI estimation for joint URLLC and eMBB resource allocation
Multiple-input multiple-output (MIMO) is a key for the fifth generation (5G) and beyond wireless communication systems owing to higher spectrum efficiency, spatial […]
Multi-Domain Network Slicing With Latency Equalization
With network slicing, physical networks are partitioned into multiple virtual networks tailored to serve different types of service with their specific requirements. […]
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 […]