Artificial Intelligence Inspired Transmission Scheduling in Cognitive Vehicular Communications and Networks
Artificial Intelligence Inspired Transmission Scheduling in Cognitive Vehicular Communications and Networks
复制标题
人工智能启发认知车辆通信和网络中的传输调度
DOI：
10.1109/jiot.2018.2872013
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发表时间：
2019-04
影响因子：
10.6
通讯作者：
Zhang Yan
中科院分区：
文献类型：
--
作者：
Zhang Ke;Leng Supeng;Peng Xin;Pan Li;Maharjan Sabita;Zhang Yan
物联网（IoT）平台通过无线通信无处不在地连接智能车辆，在提高道路运输安全和效率方面发挥了重要作用。然而，由于需要持续的通信和监控，这样的物联网模式给有限的频谱资源带来了相当大的压力。认知无线电（CR）是一种潜在的方法，以缓解频谱稀缺问题，通过机会主义利用未充分利用的频谱。然而，高度动态的拓扑结构和时变的频谱状态在基于CR的车辆网络引入了相当多的挑战要解决。此外，各种车辆通信模式，例如车辆到基础设施（V2I）和车辆到车辆（V2V），以及数据QoS要求对有效传输调度提出了关键问题。基于这一动机，本文采用深度Q学习方法来设计认知车载网络中的最佳数据传输调度方案，以最大限度地降低传输成本，同时充分利用各种通信模式和资源。此外，我们调查的通信模式和频谱资源的车辆在不同的网络状态下选择的特点，并提出了一种有效的学习算法，以获得最优的调度策略。数值结果来说明所提出的调度方案的性能。
The Internet of things (IoT) platform has played a significant role in improving road transport safety and efficiency by ubiquitously connecting intelligent vehicles through wireless communications. Such an IoT paradigm however, brings in considerable strain on limited spectrum resources due to the need.of continuous communication and monitoring. Cognitive radio (CR) is a potential approach to alleviate the spectrum scarcity problem through opportunistic exploitation of the underutilized spectrum. However, highly dynamic topology and time-varying spectrum states in CR-based vehicular networks introduce quite a few challenges to be addressed. Moreover, a variety of vehicular communication modes, such as vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V), as well as data QoS requirements pose critical issues on efficient transmission scheduling. Based on this motivation, in this paper, we adopt a deep Q-learning approach for designing an optimal data transmission scheduling scheme in cognitive vehicular networks to minimize transmission costs while also fully utilizing various communication modes and resources. Furthermore, we investigate the characteristics of communication modes and spectrum resources chosen by vehicles in different network states, and propose an efficient learning algorithm for obtaining the optimal scheduling strategies. Numerical results are presented to illustrate the performance of the proposed scheduling schemes.
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作者：
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影响因子：
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DOI：
10.1109/jstsp.2018.2798920
发表时间：
2018-02-01
影响因子：
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