Artificial Intelligence Inspired Transmission Scheduling in Cognitive Vehicular Communications and Networks
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- 24 September 2018
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Internet of Things Journal
- Vol. 6 (2), 1987-1997
- https://doi.org/10.1109/jiot.2018.2872013
Abstract
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.Keywords
Funding Information
- Fundamental Research Funds for the Central Universities, China (2672018ZYGX2018J001)
- National Natural Science Foundation of China (61374189, 61772195)
- Joint Fund of the Ministry of Education of China and China Mobile (MCM 20160304)
- Natural Science Foundation of Hunan Province (2018JJ2156)
- National Natural Science Foundation of China (61772195)
- 13th Five-Years Plan of Education Science Program of Hunan Province (XJK17BXX004)
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