Challenges and solutions for cellular based V2X communications
A wide variety of works have been conducted in vehicle-to-everything (V2X)
communications to enable a variety of applications for road safety, traffic efficiency and …
communications to enable a variety of applications for road safety, traffic efficiency and …
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 …
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) …
Spectrum sharing in vehicular networks based on multi-agent reinforcement learning
This paper investigates the spectrum sharing problem in vehicular networks based on multi-
agent reinforcement learning, where multiple vehicle-to-vehicle (V2V) links reuse the …
agent reinforcement learning, where multiple vehicle-to-vehicle (V2V) links reuse the …
Deep reinforcement learning based resource allocation for V2V communications
In this paper, we develop a novel decentralized resource allocation mechanism for vehicle-
to-vehicle (V2V) communications based on deep reinforcement learning, which can be …
to-vehicle (V2V) communications based on deep reinforcement learning, which can be …
Edge caching and computation management for real-time internet of vehicles: An online and distributed approach
J Zhao, X Sun, Q Li, X Ma - IEEE Transactions on Intelligent …, 2020 - ieeexplore.ieee.org
Vehicular Edge Computing (VEC) is expected to be an effective solution to meet the ultra-
low delay requirements of many emerging Internet of Vehicles (IoV) services by shifting the …
low delay requirements of many emerging Internet of Vehicles (IoV) services by shifting the …
Deep-learning-based wireless resource allocation with application to vehicular networks
It has been a long-held belief that judicious resource allocation is critical to mitigating
interference, improving network efficiency, and ultimately optimizing wireless communication …
interference, improving network efficiency, and ultimately optimizing wireless communication …
Toward intelligent vehicular networks: A machine learning framework
As wireless networks evolve toward high mobility and providing better support for connected
vehicles, a number of new challenges arise due to the resulting high dynamics in vehicular …
vehicles, a number of new challenges arise due to the resulting high dynamics in vehicular …
Reconfigurable intelligent surface assisted device-to-device communications
With the evolution of 5G, 6G and beyond, device-to-device (D2D) communications have
been developed as an energy-, and spectrum-efficient solution. However, D2D links are …
been developed as an energy-, and spectrum-efficient solution. However, D2D links are …
Vehicular communications: A network layer perspective
Vehicular communications, referring to information exchange among vehicles,
infrastructures, etc., have attracted a lot of attention recently due to great potential to support …
infrastructures, etc., have attracted a lot of attention recently due to great potential to support …