Energy Outage Probability and Achievable Throughput of 2-Channel Sensing Secondary Users in RF Powered Cognitive Radio Networks 


Vol. 41,  No. 9, pp. 1044-1053, Sep.  2016


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  Abstract

In this paper, we consider the secondary users (SUs) who are capable of harvesting energy from ambient radio frequency (RF) signals and are allowed to sequentially sense up to 2 different channels to find out idle channels not occupied by the primary users (PUs). The EH SUs are permitted to transmit data packets only if both idle channels and sufficient energy are available. Compared with traditional SUs, the EH SUs consume energy with data transmission and also harvest energy without additional energy supply. Consequently, the battery state is expected to be fluctuated due to energy consumption and harvesting, and therefore we develop a Markov battery model to provide energy variations at the 2-channel sensing EH SUs. With the proposed battery model, we derive the steady-state probability that the EH SUs completely run out of energy, and the achievable throughput of EH SUs is derived accordingly. To evaluate the proposed Markov battery model, the Monte-Carlo simulation was performed to validate the accuracy of energy outage probability and achievable throughput at the 2-channel sensing EH SUs.

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  Cite this article

[IEEE Style]

S. Wu, Y. Shin, D. I. Kim, "Energy Outage Probability and Achievable Throughput of 2-Channel Sensing Secondary Users in RF Powered Cognitive Radio Networks," The Journal of Korean Institute of Communications and Information Sciences, vol. 41, no. 9, pp. 1044-1053, 2016. DOI: .

[ACM Style]

Shanai Wu, Yoan Shin, and Dong In Kim. 2016. Energy Outage Probability and Achievable Throughput of 2-Channel Sensing Secondary Users in RF Powered Cognitive Radio Networks. The Journal of Korean Institute of Communications and Information Sciences, 41, 9, (2016), 1044-1053. DOI: .

[KICS Style]

Shanai Wu, Yoan Shin, Dong In Kim, "Energy Outage Probability and Achievable Throughput of 2-Channel Sensing Secondary Users in RF Powered Cognitive Radio Networks," The Journal of Korean Institute of Communications and Information Sciences, vol. 41, no. 9, pp. 1044-1053, 9. 2016.