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Optimal and Low-Complexity Dynamic Spectrum Access for RF-Powered Ambient Backscatter System With Online Reinforcement Learning

Van Huynh, Nguyen; Hoang, Dinh Thai; Nguyen, Diep N.; Dutkiewicz, Eryk; Niyato, Dusit; Wang, Ping

Authors

Nguyen Van Huynh

Dinh Thai Hoang

Diep N. Nguyen

Eryk Dutkiewicz

Dusit Niyato

Ping Wang



Abstract

Ambient backscatter has been introduced with a wide range of applications for low power wireless communications. In this paper, we propose an optimal and low-complexity dynamic spectrum access framework for the RF-powered ambient backscatter system. In this system, the secondary transmitter not only harvests energy from ambient signals but also reflects these signals to transmit its modulated data to the receiver. Under the dynamics of the ambient signals, we first adopt the Markov decision process (MDP) framework to obtain the optimal policy for the secondary transmitter, aiming to maximize the system throughput. However, the MDP-based optimization requires complete knowledge of environment parameters, e.g., the probability of a channel to be idle and the probability of a successful packet transmission, that may not be practical to obtain. To cope with such incomplete knowledge of the environment, we develop a low-complexity online reinforcement learning algorithm that allows the secondary transmitter to “learn” from its decisions and then attain the optimal policy. Simulation results show that the proposed learning algorithm not only efficiently deals with the dynamics of the environment but also improves the average throughput up to 50% and reduces the blocking probability and delay up to 80% compared with conventional methods.

Citation

Van Huynh, N., Hoang, D. T., Nguyen, D. N., Dutkiewicz, E., Niyato, D., & Wang, P. (2019). Optimal and Low-Complexity Dynamic Spectrum Access for RF-Powered Ambient Backscatter System With Online Reinforcement Learning. IEEE Transactions on Communications, 67(8), 5736-5752. https://doi.org/10.1109/tcomm.2019.2913871

Journal Article Type Article
Online Publication Date Apr 30, 2019
Publication Date 2019-08
Deposit Date Mar 29, 2023
Journal IEEE Transactions on Communications
Print ISSN 0090-6778
Electronic ISSN 1558-0857
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 67
Issue 8
Pages 5736-5752
DOI https://doi.org/10.1109/tcomm.2019.2913871
Keywords Ambient backscatter, RF energy harvesting, dynamic spectrum access, Markov decision process, reinforcement learning


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