A Hybrid Multi-Agent Reinforcement Learning Approach for Spectrum Sharing in Vehicular Networks
Efficient spectrum sharing is essential for maximizing data communication performance in
Vehicular Networks (VNs). In this article, we propose a novel hybrid framework that …
Vehicular Networks (VNs). In this article, we propose a novel hybrid framework that …
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 …
Multi-agent reinforcement learning for spectrum sharing in vehicular networks
This paper investigates the spectrum sharing problem in vehicular networks, where multiple
vehicle-to-vehicle (V2V) links reuse the frequency spectrum preoccupied by vehicle-to …
vehicle-to-vehicle (V2V) links reuse the frequency spectrum preoccupied by vehicle-to …
An IOV Spectrum Sharing Approach based on Multi-Agent Deep Reinforcement Learning
H Qian, L Cai - International Journal of Uncertainty, Fuzziness and …, 2024 - World Scientific
Highly dynamic Internet of Vehicles spectrum sharing can share spectrum owned by vehicle-
to-infrastructure links through multiple workshop links to achieve efficient resource …
to-infrastructure links through multiple workshop links to achieve efficient resource …
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) …
Joint Optimization of Spectrum and Power for Vehicular Networks: A MAPPO based Deep Reinforcement Learning Approach
W Cai, X Huang, Y Chen, Q Guan - 2024 IEEE Wireless …, 2024 - ieeexplore.ieee.org
This paper focuses on the issue of resource allocation in vehicular networking, specifically in
scenarios where vehicle-to-vehicle (V2V) links coexist with vehicle-to-infrastructure (V2I) …
scenarios where vehicle-to-vehicle (V2V) links coexist with vehicle-to-infrastructure (V2I) …
Multi-Agent Reinforcement Learning Aided Resources Allocation Method in Vehicular Networks
Y Ji, X Zhang, Y Wang, H Gacanin… - 2022 IEEE 96th …, 2022 - ieeexplore.ieee.org
To address the problem of spectrum resources and transmitting power for vehicular
networks, this paper proposes a resource allocation (RA) method based on dueling double …
networks, this paper proposes a resource allocation (RA) method based on dueling double …
Spectrum Sharing and Consensus Performance of Vehicular Networks based on Deep Multi-User Reinforcement Learning
The idea of an agent is useful for describing circumstances in which it is difficult or perhaps
impossible for a single entity to gain all the necessary knowledge about the state of a …
impossible for a single entity to gain all the necessary knowledge about the state of a …
Multi-agent reinforcement learning-based decentralized spectrum access in vehicular networks with emergent communication
In this letter, we propose a novel decentralized spectrum access algorithm based on the
multi-agent reinforcement learning (MARL) for cellular vehicle-to-everything (C-V2X) …
multi-agent reinforcement learning (MARL) for cellular vehicle-to-everything (C-V2X) …
Transfer learning in multi-agent reinforcement learning with double q-networks for distributed resource sharing in v2x communication
This paper addresses the problem of decentralized spectrum sharing in vehicle-to-
everything (V2X) communication networks. The aim is to provide resource-efficient …
everything (V2X) communication networks. The aim is to provide resource-efficient …