Adaptive Spectrum Detecting Algorithm in Cognitive Radio

  • Yun Liu
  • Qicong Peng
  • Fuchun Sun
  • Huaizong Shao
  • Xingfeng Chen
  • Ling Wang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 215)

Abstract

Cognitive radio (CR) network can make an opportunistic access of spectrum licensed to a primary user (PU). The CR must perform spectrum sensing to detect active PU, thereby avoiding interfering with it. This chapter focuses on adaptively spectrum sensing, so that negative impacts to the performance of the CR network are minimized when CR users experience both stochastic data arrival and time-varying channel. Since the frequency of the spectrum sensing has a directly impact on system throughput and the probability of collision between the PU and CR user, the PU activity is modeled based on the characteristic analysis of the PU spectrum utilization. Based on that, an efficient adaptive sensing algorithm that takes into account the system stability, collision, and throughput is proposed. The CR users can make a balance between them by utilizing a “periodic control factor” which controls the adaptive adjustment of the spectrum sensing frequency. The simulation results indicate that the proposed algorithm has excellent performance on collision probability and throughput compared with conventional periodic spectrum sensing scheme. Meanwhile, it is shown that the proposed algorithm has low implementation complexity for practical applications.

Keywords

Cognitive radio (CR) Adaptive spectrum sensing Random distribution Spectrum hole Virtual transmitting slot 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Yun Liu
    • 1
  • Qicong Peng
    • 1
  • Fuchun Sun
    • 2
  • Huaizong Shao
    • 1
  • Xingfeng Chen
    • 3
  • Ling Wang
    • 1
  1. 1.School of Communication and Technology EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.State Key Lab of Intelligent Technology and Systems, Department of Computer Science and TechnologyTsinghua UniversityBeijingChina
  3. 3.State Key Laboratory of Remote Sensing ScienceInstitute of Remote Sensing Applications, CASBeijingChina

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