Vehicular communications is one of the important applications in the low-latency and high-reliability scenarios of 5G communication systems. To meet the quality of service (QoS) requirements of different vehicle links in a dynamic environment and improve the transmission effectiveness of the communication system, a distributed Vehicle-to-Vehicle (V2V) spectrum allocation and power control scheme based on the Deep Reinforcement Learning (DRL) framework is proposed. First, the latency constraint of the V2V link is formulated as a reward function, and a positive reward is given when the constraint is not violated; then, each V2V link is regarded as an agent to realize distributed spectrum and transmission power allocation to ensure the minimum transmission overhead. We use Double Deep Q-learning (DDQN) with dueling architecture to find the mapping between the observed environment and the optimal resource allocation scheme. Compared with other algorithms, the proposed algorithm can learn to meet the strict latency constraints on the V2V link, and can effectively improve the network capacity of the Vehicle to Infrastructure (V2I) link in vehicular communications.
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