Multi-agent deep reinforcement learning-empowered channel allocation in vehicular networks
AS Kumar, L Zhao, X Fernando - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Channel allocation has a direct and profound impact on the performance of vehicle-to-
everything (V2X) networks. Considering the dynamic nature of vehicular environments, it is …
everything (V2X) networks. Considering the dynamic nature of vehicular environments, it is …
Learn to compress CSI and allocate resources in vehicular networks
Resource allocation has a direct and profound impact on the performance of vehicle-to-
everything (V2X) networks. In this paper, we develop a hybrid architecture consisting of …
everything (V2X) networks. In this paper, we develop a hybrid architecture consisting of …
Federated multi-agent deep reinforcement learning for resource allocation of vehicle-to-vehicle communications
Dynamic topology, fast-changing channels and the time sensitivity of safety-related services
present challenges to the status quo of resource allocation for cellular-underlaying vehicle …
present challenges to the status quo of resource allocation for cellular-underlaying vehicle …
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 mode selection and resource allocation for cellular V2X communications
Cellular vehicle-to-everything (V2X) communication is crucial to support future diverse
vehicular applications. However, for safety-critical applications, unstable vehicle-to-vehicle …
vehicular applications. However, for safety-critical applications, unstable vehicle-to-vehicle …
Resource allocation in vehicular communications using graph and deep reinforcement learning
Cellular based vehicle-to-everything (V2X) communications have recently gained more
interest from both academia and industry. However, there exist many challenges in cellular …
interest from both academia and industry. However, there exist many challenges in cellular …
Meta-reinforcement learning based resource allocation for dynamic V2X communications
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 …
and vehicle-to-vehicle (V2V) links in vehicle-to-everything (V2X) communications. In existing …
Multi-agent RL enables decentralized spectrum access in vehicular networks
In this paper, we investigate the joint sub-channel and power allocation problem for cellular
vehicle-to-everything (V2X) communications, where multiple vehicle-to-infrastructure (V2I) …
vehicle-to-everything (V2X) communications, where multiple vehicle-to-infrastructure (V2I) …
Resource allocation based on graph neural networks in vehicular communications
In this article, we investigate spectrum allocation in vehicle-to-everything (V2X) network. We
first express the V2X network into a graph, where each vehicle-to-vehicle (V2V) link is a …
first express the V2X network into a graph, where each vehicle-to-vehicle (V2V) link is a …
Multiagent deep-reinforcement-learning-based resource allocation for heterogeneous QoS guarantees for vehicular networks
Vehicle-to-vehicle communications can offer direct information interaction, including security-
centered information and entertainment information. However, the rapid proliferation of …
centered information and entertainment information. However, the rapid proliferation of …
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