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

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publication2020 IEEE 91st Vehicular Technology Conference, VTC Spring 2020 - Proceedings
PublisherIEEE
Number of pages6
ISBN (Electronic)9781728152073
DOIs
Publication statusPublished - May 2020
MoE publication typeA4 Article in a conference publication
EventIEEE Vehicular Technology Conference - Antwerp, Belgium
Duration: 25 May 202028 May 2020
Conference number: 91

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1090-3038
ISSN (Electronic)2577-2465

Conference

ConferenceIEEE Vehicular Technology Conference
Abbreviated titleVTC-Spring
CountryBelgium
CityAntwerp
Period25/05/202028/05/2020

Keywords

  • Ambient backscatter communications
  • Beyond 5G
  • Neural network
  • Q-learning
  • Wireless-powered

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