A4 Artikkeli konferenssijulkaisussa
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-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Jameel, Furqan; Jamshed, Muhammad Ali; Chang, Zheng; Jäntti, Riku; Pervaiz, Haris

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

Konferenssin paikka ja aika: Antwerp, Belgium, 25.-28.5.2020

ISBN: 978-1-7281-4053-7

eISBN: 978-1-7281-5207-3

Lehti tai sarja: IEEE Vehicular Technology Conference

ISSN: 1090-3038

eISSN: 2577-2465

Julkaisuvuosi: 2020

Artikkelin sivunumerot: 1-6

Kustantaja: IEEE

Julkaisumaa: Yhdysvallat (USA)

Julkaisun kieli: englanti

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

Julkaisun avoin saatavuus: Ei avoin

Julkaisukanavan avoin saatavuus: 

Tiivistelmä

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.

YSO-asiasanat: tiedonsiirto; 5G-tekniikka; tiedonsiirtotekniikat; langaton tiedonsiirto; langattomat verkot; suorituskyky; viive (tekniikka); algoritmit

Vapaat asiasanat: Q-oppiminen

Liittyvät organisaatiot

OKM-raportointi: Kyllä

Raportointivuosi: 2020

JUFO-taso: 1