QBD-Based Energy Model for RF-Powered Backscatter Cognitive Radio Networks 


Vol. 44,  No. 1, pp. 180-189, Jan.  2019
10.7840/kics.2019.44.1.180


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  Abstract

In this paper, ambient backscattering communication is considered to improve achievable throughput of secondary transmitter (ST) in RF-powered cognitive radio networks. With the capability of ambient backscattering, the ST can deliver information by reflecting the amount of energy received at the antenna. To support efficient energy harvesting and to improve achievable throughput, the ST harvests energy from the primary signals and subsequently backscatter primary signals to transmit information when the primary channel is busy. Without additional energy supply, the ST needs to harvest sufficient amount of energy to enable data transmission when the primary channel becomes idle. The residual energy at the ST will be increased or decreased according to operation mode. Therefore, we develop a quasi-birth-and-death process-based energy model to provide variations of energy states. From the developed energy model, we derive the stationary probabilities of energy outage and energy deficit. Accordingly, the achievable transmission throughput and backscatter throughput of the ST are derived. The Monte-Carlo simulation was performed to show an agreement between theoretical and simulated throughputs.

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  Cite this article

[IEEE Style]

S. Wu, J. Y. Kim, D. I. Kim, Y. Shin, "QBD-Based Energy Model for RF-Powered Backscatter Cognitive Radio Networks," The Journal of Korean Institute of Communications and Information Sciences, vol. 44, no. 1, pp. 180-189, 2019. DOI: 10.7840/kics.2019.44.1.180.

[ACM Style]

Shanai Wu, Jin Young Kim, Dong In Kim, and Yoan Shin. 2019. QBD-Based Energy Model for RF-Powered Backscatter Cognitive Radio Networks. The Journal of Korean Institute of Communications and Information Sciences, 44, 1, (2019), 180-189. DOI: 10.7840/kics.2019.44.1.180.

[KICS Style]

Shanai Wu, Jin Young Kim, Dong In Kim, Yoan Shin, "QBD-Based Energy Model for RF-Powered Backscatter Cognitive Radio Networks," The Journal of Korean Institute of Communications and Information Sciences, vol. 44, no. 1, pp. 180-189, 1. 2019. (https://doi.org/10.7840/kics.2019.44.1.180)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880