A4 Article in conference proceedings
Low Latency Ambient Backscatter Communications with Deep Q-Learning for Beyond 5G Applications (2020)

Jameel, F., Jamshed, M. A., Chang, Z., Jäntti, R., & Pervaiz, H. (2020). Low Latency Ambient Backscatter Communications with Deep Q-Learning for Beyond 5G Applications. In Proceedings of 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) (pp. 1-6). IEEE. IEEE Vehicular Technology Conference. https://doi.org/10.1109/VTC2020-Spring48590.2020.9129364

JYU authors or editors

Publication details

All authors or editors: Jameel, Furqan; Jamshed, Muhammad Ali; Chang, Zheng; Jäntti, Riku; Pervaiz, Haris

Parent publication: Proceedings of 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)

Place and date of conference: Antwerp, Belgium, 25.-28.5.2020

ISBN: 978-1-7281-4053-7

eISBN: 978-1-7281-5207-3

Journal or series: IEEE Vehicular Technology Conference

ISSN: 1090-3038

eISSN: 2577-2465

Publication year: 2020

Pages range: 1-6

Publisher: IEEE

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1109/VTC2020-Spring48590.2020.9129364

Publication open access: Not open

Publication channel open access:


Low latency is a critical requirement of beyond 5G services. Previously, the aspect of latency has been extensively analyzed in conventional and modern wireless networks. With the rapidly growing research interest in wireless-powered ambient backscatter communications, it has become ever more important to meet the delay constraints, while maximizing the achievable data rate. Therefore, to address the issue of latency in backscatter networks, this paper provides a deep Q-learning based framework for delay constrained ambient backscatter networks. To do so, a Q-learning model for ambient backscatter scenario has been developed. In addition, an algorithm has been proposed that employ deep neural networks to solve the complex Q-network. The simulation results show that the proposed approach not only improves the network performance but also meets the delay constraints for a dense backscatter network.

Keywords: data transfer; 5G technology; data transfer technologies; wireless data transmission; wireless networks; performance (capacity); delay (technology); algorithms

Free keywords: ambient backscatter communications; beyond 5G; neural network; Q-learning; wireless-powered

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2020

JUFO rating: 1

Last updated on 2022-19-08 at 19:37