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Innovative Spectrum Handoff Process Using a Machine Learning-Based Metaheuristic Algorithm.

Vikas Srivastava1,2, Parulpreet Singh1, Praveen Kumar Malik1

  • 1School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara 144411, India.

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Summary
This summary is machine-generated.

This study introduces a machine learning approach to reduce spectrum handoff issues in cognitive radio networks. The proposed SVM-RDA algorithm minimizes handoffs and delay, improving overall network performance.

Keywords:
cognitive radio networkred deer algorithmspectrum handoffspectrum sensingsupport vector machine

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Telecommunications

Background:

  • Cognitive Radio Networks (CRNs) address spectrum scarcity by enabling dynamic spectrum access.
  • Spectrum handoff (SHO) is crucial for CRNs but causes communication delays and power consumption.
  • Reducing SHO events is vital for efficient CRN operation and secondary user (SU) connectivity.

Purpose of the Study:

  • To propose a novel metaheuristic algorithm for efficient spectrum handoff management in CRNs.
  • To minimize spectrum handoff occurrences and associated delays for improved network performance.
  • To enhance the utilization of unallocated spectrum for secondary users.

Main Methods:

  • A machine learning-based metaheuristic algorithm, the Support Vector Machine-Red Deer Algorithm (SVM-RDA), is proposed.
  • Dynamic Spectrum Access (DSA) is employed to identify available channels during handoff.
  • The algorithm's performance is evaluated through simulations measuring handoffs, delay, throughput, and SNR.

Main Results:

  • The SVM-RDA algorithm demonstrates resilience and low complexity in simulation.
  • The proposed method effectively predicts handoff delay and significantly reduces the number of handoffs.
  • Experimental results show improved system performance, including higher throughput and better SNR.

Conclusions:

  • The SVM-RDA offers an effective solution for spectrum handoff management in CRNs.
  • The algorithm enhances system performance by minimizing handoffs and predicting delays.
  • This approach contributes to more reliable and efficient spectrum utilization for secondary users.