A tutorial on ultrareliable and low-latency communications in 6G: Integrating domain knowledge into deep learning
As one of the key communication scenarios in the fifth-generation and also the sixth-
generation (6G) mobile communication networks, ultrareliable and low-latency …
generation (6G) mobile communication networks, ultrareliable and low-latency …
Applications of multi-agent reinforcement learning in future internet: A comprehensive survey
Future Internet involves several emerging technologies such as 5G and beyond 5G
networks, vehicular networks, unmanned aerial vehicle (UAV) networks, and Internet of …
networks, vehicular networks, unmanned aerial vehicle (UAV) networks, and Internet of …
Multi-agent reinforcement learning based resource management in MEC-and UAV-assisted vehicular networks
In this paper, we investigate multi-dimensional resource management for unmanned aerial
vehicles (UAVs) assisted vehicular networks. To efficiently provide on-demand resource …
vehicles (UAVs) assisted vehicular networks. To efficiently provide on-demand resource …
Optimizing federated learning in distributed industrial IoT: A multi-agent approach
In this paper, we aim to make the best joint decision of device selection and computing and
spectrum resource allocation for optimizing federated learning (FL) performance in …
spectrum resource allocation for optimizing federated learning (FL) performance in …
Joint secure offloading and resource allocation for vehicular edge computing network: A multi-agent deep reinforcement learning approach
The mobile edge computing (MEC) technology can simultaneously provide high-speed
computing services for multiple vehicular users (VUs) in vehicular edge computing (VEC) …
computing services for multiple vehicular users (VUs) in vehicular edge computing (VEC) …
Split learning over wireless networks: Parallel design and resource management
Split learning (SL) is a collaborative learning framework, which can train an artificial
intelligence (AI) model between a device and an edge server by splitting the AI model into a …
intelligence (AI) model between a device and an edge server by splitting the AI model into a …
Beyond deep reinforcement learning: A tutorial on generative diffusion models in network optimization
Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of
Generative Artificial Intelligence (GAI), demonstrating their versatility and efficacy across a …
Generative Artificial Intelligence (GAI), demonstrating their versatility and efficacy across a …
Computation offloading and resource allocation in MEC-enabled integrated aerial-terrestrial vehicular networks: A reinforcement learning approach
As important services of the future sixth-generation (6G) wireless networks, vehicular
communication and mobile edge computing (MEC) have received considerable interest in …
communication and mobile edge computing (MEC) have received considerable interest in …
Mobility-aware cooperative caching in vehicular edge computing based on asynchronous federated and deep reinforcement learning
Vehicular edge computing (VEC) can learn and cache most popular contents for vehicular
users (VUs) in the roadside units (RSUs) to support real-time vehicular applications …
users (VUs) in the roadside units (RSUs) to support real-time vehicular applications …
Survey on artificial intelligence (AI) techniques for vehicular ad-hoc networks (VANETs)
Advances in communications, smart transportation systems, and computer systems have
recently opened up vast possibilities of intelligent solutions for traffic safety, convenience …
recently opened up vast possibilities of intelligent solutions for traffic safety, convenience …