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Performance Analysis of CSS Over α–η–μ and α–κ–μ Fading Channel Using Clustering-Based Technique

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Abstract

Cooperative spectrum sensing (CSS) is an efficient method, which is used to detect the presence/absence of primary user (PU) signal in the received spectrum at the secondary user (SU). Clustering is a vector quantization technique that classifies the observed signal into some fixed number of clusters depending upon the nearest mean. In this work, the K-mean clustering algorithm is used to classify the SUs received signal into the signal available class or signal unavailable class. The channel between the PU and SUs is assumed to be generalized (α–η–μ and α–κ–μ) fading channels. The performance of the proposed algorithm is compared with the conventional method of CSS with energy feature vector and found to be superior to that.

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Acknowledgements

The authors would like to thank Lt. Achint Gupta and the anonymous reviewers for their constructive comments that have improved this manuscript.

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No funding was received for this work.

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Authors and Affiliations

  1. Central Research Laboratory, BEL, Ghaziabad, India

    Sandeep Kumar

  2. Department of ECE, Pranveer Singh Institute of Technology, Kanpur, India

    Puspraj Singh Chauhan

  3. ECE Department, AKGEC, Ghaziabad, India

    Rahul Bansal

  4. Department of CSE, Delhi Technological University, Delhi, India

    Manpreet Kaur & Rajesh Kumar Yadav

Authors
  1. Sandeep Kumar
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  2. Puspraj Singh Chauhan
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  3. Rahul Bansal
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  4. Manpreet Kaur
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  5. Rajesh Kumar Yadav
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All the authors have equally contributed in this manuscript.

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Correspondence to Manpreet Kaur.

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Kumar, S., Chauhan, P.S., Bansal, R. et al. Performance Analysis of CSS Over α–η–μ and α–κ–μ Fading Channel Using Clustering-Based Technique. Wireless Pers Commun 126, 3595–3610 (2022). https://doi.org/10.1007/s11277-022-09880-y

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  • DOI: https://doi.org/10.1007/s11277-022-09880-y

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