This paper develops a novel Machine Learning (ML)-based strategy to distribute aperiodic Cooperative Awareness Messages (CAMs) through cellular Vehicle-to-Vehicle (V2V) communications. According to it, an ML algorithm is employed by each vehicle to forecast its future CAM generation times; then, the vehicle autonomously selects the radio resources for message broadcasting on the basis of the forecast provided by the algorithm. This action is combined with a wise analysis of the radio resources available for transmission, that identifies subchannels where collisions might occur, to avoid selecting them. Extensive simulations show that the accuracy in the prediction of the CAMs’ temporal pattern is excellent. Exploiting this knowledge in the strategy for radio resource assignment, and carefully identifying idle resources, allows to outperform the legacy LTE-V2X Mode 4 in all respects.

中文翻译：

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用于在蜂窝 V2V 通信中传播合作意识消息的机器学习

本文开发了一种新颖的基于机器学习 (ML) 的策略，通过蜂窝车对车 (V2V) 通信分发非周期性协作感知消息 (CAM)。据此，每辆车都采用 ML 算法来预测其未来的 CAM 生成时间；然后，车辆根据算法提供的预测，自主选择用于消息广播的无线电资源。此操作与对可用于传输的无线电资源的明智分析相结合，识别可能发生冲突的子信道，以避免选择它们。广泛的模拟表明，CAM 时间模式的预测精度非常好。在无线电资源分配策略中利用这些知识，并仔细识别空闲资源，