Most existing studies consider the deep reinforcement learning (DRL) based Q-learning
approach due to its ability to quickly converge to a near-optimal solution, resulting in …

This paper studies the allocation of shared resources between vehicle-to-infrastructure (V2I)
and vehicle-to-vehicle (V2V) links in vehicle-to-everything (V2X) communications. In existing …

Resource management in the next-generation vehicle-to-everything (V2X) communication
networks is a demanding research problem. It is difficult to achieve the best results if the …

As an essential technology of the fifth generation communication (5G), Vehicle-to-Everything
(V2X) has attracted wide attention lately. A well-designed power allocation scheme can …

Vehicular communications have recently attracted great interest due to their potential to
improve the intelligence of the transportation system. When maintaining the high reliability …

A 5G network is the key driving factor in the development of vehicle-to-vehicle (V2V)
communication technology, and V2V communication in 5G has recently attracted great …

Cellular vehicle-to-everything (C-V2X) communication is a crucial fundamental technology
to serve diverse vehicular applications. However, the diversity of communications services in …

The development of sensors and wireless communication technology has greatly promoted
the development of the Internet of Vehicles (IoV). In Vehicle to Everything (V2X) …

This research proposes a dynamic resource allocation method for vehicle-to-everything
(V2X) communications in the sixth generation (6G) cellular networks. Cellular V2X (C-V2X) …

This letter presents a deep reinforcement learning (DRL) approach for transmission design
to optimize the energy efficiency in vehicle-to-vehicle (V2V) communication links …