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:
Abstract
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