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

RF 충전 인지 무선 네트워크에서 2-채널 센싱 2차 사용자의 Energy Outage 확률 및 패킷 전송 성능

  • Wu, Shanai (Soongsil University, School of Electronic Engineering) ;
  • Shin, Yoan (Soongsil University, School of Electronic Engineering) ;
  • Kim, Dong In (Sungkyunkwan University, School of Information and Communication Engineering)
  • Received : 2016.08.13
  • Accepted : 2016.09.05
  • Published : 2016.09.30

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.

본 논문에서는 인지 무선 (Cognitive Radio; CR) 네트워크에서 Radio Frequency (RF) 에너지 수집 (Energy Harvesting; EH) 기능을 갖는 2차 사용자 (Secondary User; SU)가 최대 2개의 서로 다른 채널을 순차적으로 센싱하여 주사용자 (Primary User; PU)가 사용하지 않고 비어 있는 채널을 확보하는 경우를 고려하였다. EH SU는 데이터를 전송하기 위해 비어 있는 채널을 정확하게 검출해야 할 뿐만 아니라 충분한 에너지를 보유하고 있어야 한다. 기존의 SU와 마찬가지로 데이터 전송에 의한 에너지 소모와 더불어 자체적으로 에너지 수집이 가능하기 때문에 EH SU의 에너지 상태는 감소와 증가를 반복하게 되며, 본 논문에서는 이와 같은 EH SU의 배터리 상태를 Markov 모델로 구축하였다. 해당 모델로부터 EH SU가 에너지를 완전히 소모할 안정상태 확률을 도출하였으며, 이에 근거하여 패킷을 성공적으로 전송할 확률을 도출하였다. 제안된 Markov 배터리 모델을 분석하기 위해 Monte-Carlo 모의실험을 진행하여 Energy Outage 확률과 패킷 전송 성능 분석의 정확성을 검증하였다.

Keywords

References

  1. X. Lu, P. Wang, D. Niyato, D. I. Kim, and A. Han, "Wireless networks with RF energy harvesting: A contemporary survey," IEEE Commun. Surveys & Tuts., vol. 17, no. 2, pp. 757-789, May 2015. https://doi.org/10.1109/COMST.2014.2368999
  2. X. Lu, D. Niyato, P. Wang, D. I. Kim, and Z. Han, "Wireless charger networking for mobile devices: Fundamentals, standards, and applications," IEEE Wireless Commun., vol. 22, no. 2, pp. 126-135, Apr. 2015. https://doi.org/10.1109/MWC.2015.7096295
  3. S. Kim, R. Vyas, J. Bito, K. Nitotaki, A. Collado, A. georigiadis, and M. M. Tentzeris, "Ambient RF energy-harvesting technologies for self-sustainable standalone wireless sensor platforms," IEEE Proc., vol. 102, no. 11, pp. 1649-1666, Nov. 2014. https://doi.org/10.1109/JPROC.2014.2357031
  4. A. A. Aziz, D. Tribudi, L. Ginting, P. A. Rosyady, D. Setiawan, and K. Choi, "RF energy transfer testbed based on off-the-shelf components for IoT application," J. KICS, vol. 40, no. 10, pp. 1912-1921, Oct. 2015. https://doi.org/10.7840/kics.2015.40.10.1912
  5. M. Kim, W. Lim, J. Bae, J. Park, Y. Park, J. Lee, S. Trinh-Van, D. I. Kim, K. Lee, K. Hwang, and Y. Yang, "Development of far field RF power harvesting testbed," J. KICS, vol. 40, no. 10, pp. 1922-1930, Oct. 2015. https://doi.org/10.7840/kics.2015.40.10.1922
  6. S. Choi and D. I. Kim, "Backscatter communication for wireless-powered communication networks," J. KICS, vol. 40, no. 10, pp. 1900-1911, Oct. 2015. https://doi.org/10.7840/kics.2015.40.10.1900
  7. Y.-C. Liang, K.-C. Chen, G. Y. Li, and P. Mahonen, "Cognitive radio networking and communications: An overview," IEEE Trans. Veh. Technol., vol. 60, no. 7, pp. 3386-3407, Sept. 2011. https://doi.org/10.1109/TVT.2011.2158673
  8. X. Lu, P. Wang, D. Niyato, and E. Hossain, "Dynamic spectrum access in cognitive radio networks with RF energy harvesting," IEEE Wireless Commun., vol. 21, no. 7, pp. 102-110, Jun. 2014. https://doi.org/10.1109/MWC.2014.6845054
  9. D. Hoang, D. Niyato, P. Wang, and D. I. Kim, "Opportunistic channel access and RF energy harvesting in cognitive radio networks," IEEE J. Sel. Areas in Commun., vol. 32, no. 11, pp. 2039-2052, Nov. 2014. https://doi.org/10.1109/JSAC.2014.141108
  10. S. Park, H. Kim, and D. Hong, "Cognitive radio networks with energy harvesting," IEEE Trans. Wireless Commun., vol. 12, no. 3, pp. 1386-1397, Mar. 2013. https://doi.org/10.1109/TWC.2013.012413.121009
  11. W. Chung, S. Park, S. Lim, and D. Hong, "Spectrum sensing optimization for energy-harvesting cognitive radio systems," IEEE Trans. Wireless Commun., vol. 13, no. 5, pp. 2601-2613, May 2014. https://doi.org/10.1109/TWC.2014.032514.130637
  12. S. Park and D. Hong, "Achievable throughput of energy harvesting cognitive radio networks," IEEE Trans. Wireless Commun., vol. 13, no. 2, pp. 1010-1022, Feb. 2014. https://doi.org/10.1109/TWC.2013.121713.130820
  13. D. Hoang, D. Niyato, P. Wang, and D. I. Kim, "Performance optimization for cooperative multiuser cognitive radio networks with RF energy harvesting capability," IEEE Trans. Wireless Commun., vol. 14, no. 7, pp. 3614-3629, Jul. 2015. https://doi.org/10.1109/TWC.2015.2408610
  14. S. Wu, Y. Shin, J. Y. Kim, and D. I. Kim, "Probability of packet loss in energy harvesting nodes with cognitive radio capabilities," IEEE Commun., Lett., vol. 20, no. 5, pp. 978-981, May 2016. https://doi.org/10.1109/LCOMM.2016.2539158
  15. Y.-C. Liang, Y. Zeng, E. C. Y. Peh, and A. T. Hoang, "Sensing-throughput tradeoff for cognitive radio networks," IEEE Trans. Wireless Commun., vol. 7, no. 4, pp. 1326-1337, Apr. 2008. https://doi.org/10.1109/TWC.2008.060869