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 editorsJameel, Furqan; Jamshed, Muhammad Ali; Chang, Zheng; Jäntti, Riku; Pervaiz, Haris

Parent publicationProceedings of 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)

Place and date of conferenceAntwerp, Belgium25.-28.5.2020

ISBN978-1-7281-4053-7

eISBN978-1-7281-5207-3

Journal or seriesIEEE Vehicular Technology Conference

ISSN1090-3038

eISSN2577-2465

Publication year2020

Pages range1-6

PublisherIEEE

Publication countryUnited States

Publication languageEnglish

DOIhttps://doi.org/10.1109/VTC2020-Spring48590.2020.9129364

Publication open accessNot 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.


Keywordsdata transfer5G technologydata transfer technologieswireless data transmissionwireless networksperformance (capacity)delay (technology)algorithms

Free keywordsambient backscatter communications; beyond 5G; neural network; Q-learning; wireless-powered


Contributing organizations


Ministry reportingYes

Reporting Year2020

JUFO rating1


Last updated on 2024-23-02 at 19:31