The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Artificial Intelligence Inspired Transmission Scheduling in Cognitive Vehicular Communications and Networks
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 and vehicle-to-vehicle, 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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Cites
Playing Atari with Deep Reinforcement Learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv · doi:10.48550/arxiv.1312.5602 · Cited by 9,471
Deep Reinforcement Learning with Double Q-Learning
Van Hasselt, H., Guez, A., & Silver, D. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10295 · Cited by 3,318
Cited by
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Wang, C.-X., You, X., Gao, X., Zhu, X., Li, Z., Zhang, C., Wang, H., Huang, Y., Chen, Y., Haas, H., et al. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3249835 · Cited by 2,336
Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT
Lu, Y., Huang, X., Dai, Y., Maharjan, S., & Zhang, Y. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2942190 · Cited by 1,245
Self-citationBlockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles
Lu, Y., Huang, X., Zhang, K., Maharjan, S., & Zhang, Y. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.2973651 · Cited by 733
Self-citationXu, Y., Gui, G., Gacanin, H., & Adachi, F. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3059896 · Cited by 648
Lu, Y., Huang, X., Dai, Y., Maharjan, S., & Zhang, Y. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2942179 · Cited by 387
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Metadata
- Authors
- Zhang, K., Leng, S., Peng, X., Pan, L., Maharjan, S., & Zhang, Y.
- Venue
- IEEE Internet of Things Journal
- Published
- 2019-04
- Volume
- 6
- Issue
- 2
- Pages
- 1987-1997
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W2894383926
- Cited by
- 162
- References
- 2
- FWCI
- 9.03
- Type
- journal article
- Language
- en
- Open access
- closed
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