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