Journal of information and communication convergence engineering

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Throughput Maximization for Cognitive Radio Users with Energy Constraints in an Underlay Paradigm

  • Vu, Van-Hiep (School of Electrical Engineering, University of Ulsan) ;
  • Koo, Insoo (School of Electrical Engineering, University of Ulsan)
  • Received : 2017.05.11
  • Accepted : 2017.06.01
  • Published : 2017.06.30
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Abstract

In a cognitive radio network (CRN), cognitive radio users (CUs) should be powered by a small battery for their operations. The operations of the CU often include spectrum sensing and data transmission. The spectrum sensing process may help the CU avoid a collision with the primary user (PU) and may save the energy that is wasted in transmitting data when the PU is present. However, in a time-slotted manner, the sensing process consumes energy and reduces the time for transmitting data, which degrades the achieved throughput of the CRN. Subsequently, the sensing process does not always offer an advantage in regards to throughput to the CRN. In this paper, we propose a scheme to find an optimal policy (i.e., perform spectrum sensing before transmitting data or transmit data without the sensing process) for maximizing the achieved throughput of the CRN. In the proposed scheme, the data collection period is considered as the main factor effecting on the optimal policy. Simulation results show the advantages of the optimal policy.

Keywords

References

  1. Y. Hur, J. Park, W. Woo, K. Lim, C. H. Lee, H. S. Kim, and J. Laskar, "A wideband analog multi-resolution spectrum sensing (MRSS) technique for cognitive radio (CR) systems," in Proceedings of IEEE International Symposium on Circuit and System (ISCAS), Island of Kos, Greece, pp. 4090-4093, 2006.
  2. A. Sahai, N. Hoven, and R. Tandra, "Some fundamental limits on cognitive radio," in Proceedings of Allerton Conference on Communications, Control, and Computing, Monticello, IL, pp. 1- 11, 2004.
  3. Z. Shi, T. Tan, K. C. The, and K. H. Li, "Energy efficient cognitive radio network based on multiband sensing and spectrum sharing," IET Communications, vol. 8, no. 9, pp. 1499-1507, 2014. https://doi.org/10.1049/iet-com.2013.0699
  4. M. Zhou and X. Zhao, "Energy-efficient power allocation algorithm in cognitive radio networks," IET Communications, vol. 10, no. 17, pp. 2445-2451, 2016. https://doi.org/10.1049/iet-com.2016.0274
  5. S. Mao, M. H. Cheung, and V. Wong, "An optimal energy allocation algorithm for energy harvesting wireless sensor networks," in Proceedings of IEEE International Conference on Communications (ICC), Ottawa, Canada, pp. 265-270, 2012.
  6. Y. C. Liang, Y. Zeng, E. Peh, and A. T. Hoang, "Sensingthroughput tradeoff for cognitive radio networks," IEEE Transactions on Wireless Communications, vol. 7, no. 4, pp. 1326- 1337, 2008. https://doi.org/10.1109/TWC.2008.060869
  7. A. Sultan, "Sensing and transmit energy optimization for an energy harvesting cognitive radio," IEEE Wireless Communications Letters, vol. 1, no. 5, pp. 500-503, 2012. https://doi.org/10.1109/WCL.2012.071612.120304
  8. S. Park, J. Heo, B. Kim, W. Chung, H. Wang, and D. Hong, "Optimal mode selection for cognitive radio sensor networks with RF energy harvesting," in Proceedings of 2012 IEEE 23rd International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), Sydney, Australia, pp. 2155-2159, 2012.
  9. X. Cao and X. Guo, "Partially observable Markov decision processes with reward information," in Proceedings of 43rd IEEE Conference on Decision and Control (CDC), Nassau, Bahamas, pp. 4393-4398, 2004.
  10. L. P. Kaelbling, M. L. Littman, and A. R. Cassandra, "Planning and acting in partially observable stochastic domains," Artificial Intelligence, vol. 101, no. 1, pp. 99-134, 1998. https://doi.org/10.1016/S0004-3702(98)00023-X
  11. J. Ma and Y. Li, "Soft combination and detection for cooperative spectrum sensing in cognitive radio networks," in Proceedings of IEEE Global Telecommunications Conference (GLOBECOM), Washington, DC, pp. 3139-3143, 2007.
  12. D. P. Bertsekas, Dynamic Programming and Optimal Control, 2nd ed. Belmont, MA: Athena Scientific, 2001.

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