Low Latency Ambient Backscatter Communications with Deep Q-Learning for beyond 5G Applications

Furqan Jameel, Muhammad Ali Jamshed, Zheng Chang, Riku Jäntti, Haris Pervaiz

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

8 Sitaatiot (Scopus)


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.

Otsikko2020 IEEE 91st Vehicular Technology Conference, VTC Spring 2020 - Proceedings
ISBN (elektroninen)978-1-7281-5207-3
DOI - pysyväislinkit
TilaJulkaistu - toukok. 2020
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaIEEE Vehicular Technology Conference - Antwerp, Belgia
Kesto: 25 toukok. 202028 toukok. 2020
Konferenssinumero: 91


NimiIEEE Vehicular Technology Conference
ISSN (painettu)1090-3038
ISSN (elektroninen)2577-2465


ConferenceIEEE Vehicular Technology Conference


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