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Sensing Confidence Level-Based Joint Spectrum and Power Allocation in Cognitive Radio Networks

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Abstract

Cognitive radio (CR) has been extensively investigated in the past decade to tackle the contradiction between wireless spectrum shortage and underutilization. In this paper, we present a unified analytical framework to design PHY-layer spectrum sensing and MAC-layer resource scheduling jointly for CR networks. A key parameter, named sensing confidence level (SCL), is introduced to characterize the presence of imperfect sensing, and bridge the designs between kinds of spectrum sensing schemes and resource allocation algorithms. The SCL-based joint design of spectrum and power allocation is formulated as a mixed integer non-linear optimization problem and Lagrange duality theory is introduced to make the problem tractable. The proposed joint design framework in this paper provides a baseline for comparing different spectrum sensing schemes plus bandwidth and power allocation algorithms. Numerical results demonstrate the effectiveness of the proposed framework.

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Abbreviations

CSS:

Cooperative spectrum sensing

CR:

Cognitive radio

PN:

Primary network

CRN:

Cognitive radio network

SU:

Secondary user

PU:

Primary user

SSI:

Soft sensing information

HSI:

Hard sensing information

RSI:

Raw sensing information

SBS:

secondary base station

LHGH:

Local one-bit hard decision and global one-bit hard decision

LHGS:

Local one-bit hard decision and global soft fusion

LSGS:

local soft sensing and global soft fusion

CSI:

Channel state information

SCL:

Sensing confidence level

SEP:

Sensing error percentage

References

  1. Wu, T. Bandwidth is the new black gold—10 ideas for the next 10 years. http://www.time.com/time/specials/packages/article/0,28804,1971133_1971110_1971125,00.html.

  2. Mitola, J. (2000). Cognitive radio: An integrated agent architecture for software defined radio. Dissertation, Royal Institute Technology (KTH) Stockholm, Sweden.

  3. Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communication, 23(2), 201–220.

    Article  Google Scholar 

  4. Yucek, T., & Arslan, H. A. (2009). Survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communications Survey and Tutorials, 11(1), 116–130.

    Article  Google Scholar 

  5. Bixio, L., Ottonello, M., Raffetto, M., & Regazzoni, C. S. (2011). Comparison and cognitive radio architectures for spectrum sensing. EURASIP Journal on Wireless Communications and Networking, 2011, Article ID 749891, 18. doi:10.1155/2011/759891.

  6. Akyildiz, I. F., Brandon, F. L., & Balakrishnan, R. (2011). Cooperative spectrum sensing in cognitive radio networks: A survey. Physical Communications, 4, 40–62.

    Article  Google Scholar 

  7. Zhang, R., Liang, Y.-C., & Cui, S. (2010). Dynamic resource allocation in cognitive radio networks. IEEE Signal Processing Magazine, 27(3), 102–114.

    Article  MathSciNet  Google Scholar 

  8. Huang, D., Shen, Z., Miao, C., & Leung, C. (2010). Resource allocation in MU-OFDM cognitive radio systems with partial channel state information. EURASIP Journal on Wireless Communications and Networking, 2010, Article ID 189157, 8. doi:10.1155/2010/189157.

  9. Wang, B., & Liu, K. J. R. (2011). Advances in cognitive radio networks: A survey. IEEE Journal of Selected Topics in Signal Processing, 5(1), 5–23.

    Article  Google Scholar 

  10. Cheng, P., Zhang, Z., Chen, H. H., & Qiu, P. (2008). Optimal distributed joint frequency, rate and power allocation in cognitive OFDMA systems. IET Communications, 2(6), 815–826.

    Article  Google Scholar 

  11. Peh, E. C. Y., Liang, Y.-C., Guan, Y. L., & Zeng, Y. (2009). Optimization of cooperative sensing in cognitive radio networks: A sensing-throughput tradeoff view. IEEE Transactions on Vehicular Technology, 58(9), 5294–5299.

    Article  Google Scholar 

  12. Almalfouh, S. M., & Stüber, G. L. (2011). Interference-aware radio resource allocation in OFDMA-based cognitive radio networks. IEEE Transactions on Vehicular Technology, 60(4), 1699–1713.

    Article  Google Scholar 

  13. Chen, Y., Zhao, Q., & Swami, A. (2008). Joint design and separation principle for opportunistic spectrum access in the presence of sensing errors. IEEE Transactions on Information Theory, 54(5), 2053–2071.

    Article  MathSciNet  Google Scholar 

  14. Wang, R., & Lau, Vincent K. N. (2009). Joint cross-layer scheduling and spectrum sensing for OFDMA cognitive radio systems. IEEE Transactions on Wireless Communications, 8(5), 2410–2416.

    Article  Google Scholar 

  15. Ding, G., Wu, Q., Wang, J., & Zhang, X. (2010). Joint cooperative spectrum sensing and resource scheduling for cognitive radio networks with soft sensing information. In Proceedings of the IEEE YC-ICT’10.

  16. Fan, R., Jiang, H., Guo, Q., & Zhang, Z. (2011). Joint optimal cooperative sensing and resource allocation in multichannel cognitive radio networks. IEEE Transactions on Vehicular Technology, 60(2), 722–729.

    Article  Google Scholar 

  17. Park, J., Pawełczak, P., & Cabric, D. (2011). Performance of joint spectrum sensing and MAC algorithms for multichannel opportunistic spectrum access ad hoc networks. IEEE Transactions on Mobile Computing, 10(7), 1011–1027.

    Article  Google Scholar 

  18. Lo Brandon, F. A. (2011). Survey of common control channel design in cognitive radio networks. Physical Communication, 4, 26–39. doi:10.1016/j.phycom.2010.12.004.

  19. Cormio, C., & Chowdhury, K. R. (2010). Common control channel design for cognitive radio wireless ad hoc networks using adaptive frequency hopping. Ad Hoc Networks, 8, 430–438. doi:10.1016/j.adhoc.2009.10.004.

    Article  Google Scholar 

  20. Chowdhury, K. R., & Akyildiz, I. F. (2011). OFDM-based common control channel design for cognitive radio ad hoc networks. IEEE Transactions on Mobile Computing, 10(2), 228–238.

    Article  Google Scholar 

  21. Urkowitz, H. (1967). Energy detection of unknown deterministic signals. Proceedings of the IEEE, 55(4), 523–531.

    Article  Google Scholar 

  22. Letaief, K. B., & Zhang, W. (2009). Cooperative communications for cognitive radio networks. Proceedings of the IEEE, 97(5), 878–893.

    Article  Google Scholar 

  23. Quan, Z., Cui, S., & Sayed, A. H. (2008). Optimal linear cooperation for spectrum sensing in cognitive radio networks. IEEE Journal on Selected Topics and Signal Processing, 2(1), 28–40.

    Article  Google Scholar 

  24. Ma, J., Zhao, G., & Li, Y. (2008). Soft combination and detection for cooperative spectrum sensing in cognitive radio networks. IEEE Transactions on Wireless Communication, 7(11), 4502–4507.

    Article  Google Scholar 

  25. Ghasemi, A., & Sousa, E. S. (2007). Fundamental limits of spectrum-sharing in fading environments. IEEE Transactions on Wireless Communication, 6(2), 649–658.

    Article  Google Scholar 

  26. Zhao, G., Li, G., & Yang, C. (2009). Proactive detection of spectrum opportunities in primary systems with power control. IEEE Transactions on Wireless Communication, 8(9), 4815–4823.

    Article  Google Scholar 

  27. Zhang, R. (2010). On active learning and supervised transmission of spectrum sharing based cognitive radios by exploiting hidden primary radio feedback. IEEE Transactions on Communication, 58(10), 2960–2970.

    Article  Google Scholar 

  28. Eswaran, K., Gastpar, M., & Ramchandran, K. (2011). Cognitive radio through primary control feedback. IEEE Journal on Selected Areas in Communications, 29(2), 384–393.

    Article  Google Scholar 

  29. Huang, S., Liu, X., & Ding, Z. (2011). Decentralized cognitive radio control based on inference from primary link control information. IEEE Journal on Selected Areas in Communications, 29(2), 394–406.

    Article  Google Scholar 

  30. Wu, Q., Huang, Y., Wang, J., & Cheng, Y. (2012). Effective capacity of cognitive radio systems with GSC diversity under imperfect channel knowledge. IEEE Communications Letters, 16(11), 1792–1795.

    Google Scholar 

  31. Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge: Cambridge University Press.

    Book  MATH  Google Scholar 

  32. Wei, Y., & Lui, R. (2006). Dual methods for nonconvex spectrum optimization of multicarrier systems. IEEE Transactions on Communication, 54, 1310–1322.

    Article  Google Scholar 

  33. Antonio, G. M., Wang, X., & Georgios, B. G. (2009). Dynaimic resource management for cognitive radios using limited-rate feedback. IEEE Transactions on Signal Processing, 57(9), 3651–3666.

    Article  MathSciNet  Google Scholar 

  34. Stevenson, C. R., Chouinard, G., Lei, Z., Hu, W., Shellhammer, S. J., & Caldwell, W. (2009). IEEE 802.22: The first cognitive radio wireless regional area network standard. IEEE Communications on Magazine, 2009, 130–138.

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the associate editor and the anonymous reviewers for their precious time and constructive comments, which have greatly improved the quality of this article. This work was supported in part by the National Basic Research Program of China (No. 2009CB320400), the National Natural Science Foundation of China (Nos. 60932002 and 6117206), and the Natural Science Foundation of Jiangsu, China (No. BK2011116).

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  1. College of Communications Engineering, PLA University of Science and Technology, Nanjing,  210007, China

    Guoru Ding, Qihui Wu & Jinlong Wang

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  1. Guoru Ding
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Correspondence to Guoru Ding.

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Ding, G., Wu, Q. & Wang, J. Sensing Confidence Level-Based Joint Spectrum and Power Allocation in Cognitive Radio Networks. Wireless Pers Commun 72, 283–298 (2013). https://doi.org/10.1007/s11277-013-1013-3

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