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Optimized Extreme Learning Machine for Intelligent Spectrum Sensing in 5G systems

  • NOVEL RADIO SYSTEMS AND ELEMENTS
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Abstract

A two-level learned distributed networking (LDN) structure that uses existing machine learning (ML) algorithms and the novel Optimized Extreme Learning Machine (OELM) algorithm to perform intelligent spectrum sensing for 5G systems has been proposed and implemented. This novel technique uses input vectors like received signal strength indicator, the distance between Cognitive Radio users and gateways, and energy vectors to train the model. Extreme Learning Machine optimized by BAT algorithm outperforms the existing Machine Learning techniques in terms of detection accuracy, false alarm, detection probability and cross validation curves at different SNR scenarios.

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ACKNOWLEDGMENTS

The authors would like to acknowledge the help given by the lab staff at the Department of ECE, Indira Gandhi Delhi Technical University for solving any software and system malfunctions.

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Authors and Affiliations

  1. Indira Gandhi Delhi Technical University, New Church Road, Opp. St, Kashmere Gate, 110006, New Delhi, Delhi, India

    P. Kansal & A. Kumar

  2. Ambedkar Institute of Advanced Communication Technologies and Research, Krishna Nagar Road Chacha Nahru Bal Chikitsalaya, Geeta Colony, 110031, New Delhi, Delhi, India

    M. Gangadharappa

Authors
  1. P. Kansal
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  2. A. Kumar
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  3. M. Gangadharappa
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Correspondence to P. Kansal.

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Kansal, P., Kumar, A. & Gangadharappa, M. Optimized Extreme Learning Machine for Intelligent Spectrum Sensing in 5G systems. J. Commun. Technol. Electron. 66, 322–332 (2021). https://doi.org/10.1134/S1064226921040045

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  • DOI: https://doi.org/10.1134/S1064226921040045

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