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Performance Analysis of Various Eigenvalue-Based Spectrum Sensing Algorithms for Different Types of Primary User Signals

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Advances in Electronics, Communication and Computing

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 443))

Abstract

Spectrum sensing plays a very essential role in the implementation of cognitive radio networks. Eigenvalue-based spectrum sensing algorithms have been comprehensively discussed in the literature, for detection of primary user signal in the case of uncertain noise. For detection of signals, the test statistics of these algorithms depend on the eigenvalues of the covariance matrix of the received signal. Eigenvalues generally capture the correlation among the signal samples. In this context, we have examined the sensing performance of various eigenvalue-based spectrum sensing techniques for different types of primary user signals having different levels of correlation. In results, it has been noticed that the sensing performance of the algorithms relies on the type of primary user signal transmitted.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology Kurukshetra, Kurukshetra, India

    Pankaj Verma & Brahmjit Singh

Authors
  1. Pankaj Verma
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  2. Brahmjit Singh
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Corresponding author

Correspondence to Pankaj Verma .

Editor information

Editors and Affiliations

  1. Smart Energy Research Unit, College of Engineering and Science, Victoria University, Melbourne, VIC, Australia

    Akhtar Kalam

  2. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Swagatam Das

  3. Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Rangpo, Sikkim, India

    Kalpana Sharma

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Verma, P., Singh, B. (2018). Performance Analysis of Various Eigenvalue-Based Spectrum Sensing Algorithms for Different Types of Primary User Signals. In: Kalam, A., Das, S., Sharma, K. (eds) Advances in Electronics, Communication and Computing. Lecture Notes in Electrical Engineering, vol 443. Springer, Singapore. https://doi.org/10.1007/978-981-10-4765-7_41

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  • DOI: https://doi.org/10.1007/978-981-10-4765-7_41

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-4764-0

  • Online ISBN: 978-981-10-4765-7

  • eBook Packages: EngineeringEngineering (R0)

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