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Abstract—The rapid changes in high-mobility vehicle envi- ronments make it challenging for base stations (BS) to obtain.
To address these challenges, this paper proposes a method combining dueling double deep-Q network (D3QN) reinforcement learning (RL) with long short term memory ...
The experimental results indicate that the proposed method outperforms the conventional D3QN network in both the vehicle-to-infrastructure (V2I) links and ...
Sep 28, 2024 · Resource Allocation Based on Reinforcement Learning for Heterogeneous Air Network · Radio Resource Allocation Method for Network Slicing using ...
Jan 17, 2024 · In this paper, we propose a novel multi-agent deep reinforcement learning (DRL) and dual network structure based resource allocation strategy for vehicular ...
This paper proposes a resource allocation algorithm for vehicular networks which utilizes multi-agent deep reinforcement learning, specifically designed for the ...
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Jan 2, 2025 · Recent advancements in vehicular networks have garnered significant attention due to their potential to improve road safety, optimize resource ...
This pioneering approach optimizes spectrum utilization, ushering in superior service quality for high-speed mobile vehicles in vehicular networks.
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Enhanced Resource Allocation in Vehicular Networks via Multi-Agent Reinforcement Learning. Conference Paper. Jun 2024. Yu Zhang · Shufei Wang · Minyu Hua ...