A DDQN-based Energy-Efficient Resource Allocation Scheme for Low-Latency V2V communication

Miao, Juanjuan; Chai, Xinyue; Song, Xiaolong; Song, Tiechen

doi:10.1109/cieec54735.2022.9846189

Cited by 6 publications

(1 citation statement)

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“…However, this scheme primarily focuses on worst-case delay minimization, and its performance in other scenarios needs further investigation. To address modeling accuracy concerns, [16] [17] model resource sharing as a multi-agent reinforcement learning problem and employ deep Q-learning algorithms for joint channel assignment and power allocation design. It is worth noting that the aforementioned works primarily concentrate on minimizing delay and maximizing throughput, No concern for link security on in-vehicle networks.…”

Section: A Related Workmentioning

confidence: 99%

Optimization of Resource Allocation for V2X Security Communication based on Multi-Agent Reinforcement Learning

Ji,

Dong,

et al. 2024

IEEE Trans. Veh. Technol.

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In order to address the data security and communication efficiency of vehicles during high-speed mobile communication, this paper investigates the problem of secure invehicle communication resource allocation based on slow-variable large-scale fading channel information, to meet the quality of service requirements of vehicular communication, i.e., to ensure the reliability of V2V communication and the time delay while maximizing the transmission rate of the cellular link. And an eavesdropping model is introduced to ensure the secure delivery of link information. Considering that the high mobility of vehicles causes rapid channel changes, we model the problem as a Markov decision process and propose a resource allocation optimization framework based on the Multi-Agent Reinforcement Learning Algorithm (MARL-DDQN), in which a large-scale neural network model is built to train vehicular intelligences to learn the optimal resource allocation strategy for optimal communication performance and security performance. Simulation results show that the load successful delivery rate and confidentiality performance of the vehicular communication network are effectively improved compared to the baseline and MADDPG strategies while ensuring link security. This study provides useful references and practical value for the optimization of secure communication resource allocation in vehicular networking.

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Section: A Related Workmentioning

confidence: 99%