Abstract
We investigate machine-learning-based cross-layer energy-efficient transmission design for vehicular communication systems. A typical vehicle-to-vehicle (V2V) communication scenario is considered, in which the source intends to deliver two types of messages to the destination to support different safety-related applications. The first are periodically-generated heartbeat messages, and should be transmitted immediately with sufficient reliability. The second type are randomly-appeared sensing messages, and are expected to be transmitted with limited latency. Due to node mobility, accurate instantaneous channel knowledge at the transmitter side is hard to attain in practice. The transmit channel state information (CSIT) often exhibits certain delay. We propose a transmission strategy based on the deep reinforcement learning technique such that the unknown channel variation dynamics can be learned and transmission power and rate can be adaptive chosen according to the message delay status to achieve high energy efficiency. The advantages of our method over several conventional and heuristic approaches are demonstrated through computer simulations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cao, B., Zhang, L., Li, Y., Feng, D., Cao, W.: Intelligent offloading in multi-access edge computing: a state-of-the-art review and framework. IEEE Commun. Mag. 57(3), 56–62 (2019)
Jiang, C., Zhang, H., Ren, Y., Han, Z., Chen, K., Hanzo, L.: Machine learning paradigms for next-generation wireless networks. IEEE Wirel. Commun. 24(2), 98–105 (2016)
Zhang, C., Patras, P., Haddadi, H.: Deep learning in mobile and wireless networking: a survey. IEEE Commun. Surv. Tutor. 21(3), 2224–2287 (2019)
Lan, D., Wang, C., Wang, P., Liu, F., Min, G.: Transmission design for energy-efficient vehicular networks with multiple delay-limited applications. In: 2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa, USA, December 2019
Hasselt, H., Guez, A., Silver, D.: Deep reinforcement learning with double Q-learning. In: Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, Arizona, USA, February 2016
Seo, H., Lee, K., Yasukawa, S., Peng, Y., Sartori, P.: LTE evolution for vehicle-to-everything services. IEEE Commun. Mag. 54(6), 22–28 (2016)
Dar, K., Bakhouya, M., Gaber, J., Wack, M., Pascal, L.: Wireless communication technologies for ITS applications. IEEE Commun. Mag. 48(5), 156–162 (2010)
Crites, R., Barto, A.: Improving elevator performance using reinforcement learning. In: Advances in Neural Information Processing Systems, pp. 1017–1023 (1996)
Sutton, R., Barto, A.: Introduction to Reinforcement Learning. MIT Press, Cambridge (2018)
Li, S., Xu, L., Zhao, S.: 5G internet of things: a survey. J. Ind. Inf. Integr. 10, 1–9 (2018)
Lillicrap, T., et al.: Continuous control with deep reinforcement learning. CoRR abs/1509.02971 (2015)
Mnih, V., et al.: Playing Atari with deep reinforcement learning. NeurlIPS (2013)
Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529–533 (2015)
Sun, W., Ström, E., Brännström, F., Sou, K., Sui, Y.: Radio resource management for D2D-based V2V communication. IEEE Trans. Veh. Technol. 65(8), 6636–6650 (2015)
Zhan, W., Luo, C., Wang, J., Wang, C., Min, G., Duan, H., Zhu, Q.: Deep reinforcement learning-based offloading scheduling for vehicular edge computing. IEEE Internet Things J. 7(6), 5449–5465 (2020)
Acknowledgement
This work was supported in part by the National Natural Science Foundation of China under Grant 61771343, and the Intelligent Connected Vehicle Pilot Demonstration Project under grant 2019B090912002.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Yu, S., Qu, N., Zhang, Y., Wang, C., Liu, F. (2021). A Transmission Design via Reinforcement Learning for Delay-Aware V2V Communications. In: Gao, H., Fan, P., Wun, J., Xiaoping, X., Yu, J., Wang, Y. (eds) Communications and Networking. ChinaCom 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 352. Springer, Cham. https://doi.org/10.1007/978-3-030-67720-6_42
Download citation
DOI: https://doi.org/10.1007/978-3-030-67720-6_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-67719-0
Online ISBN: 978-3-030-67720-6
eBook Packages: Computer ScienceComputer Science (R0)