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<p>Spectrum sensing is a critical function in cognitive radio networks, enabling the identification of available frequency bands without interfering with primary users. To improve the effectiveness of energy detection, we propose an <b>adaptive double-threshold method</b> that dynamically adjusts the upper and lower thresholds based on the signal-to-noise ratio (SNR) of cognitive nodes. This research introduces a <b>novel framework for determining the optimal weighting coefficients</b> necessary for these threshold adjustments. Specifically, we present the <b>hybrid Whale-Chimp Optimization Algorithm (WCOA)</b>, which ensures stable threshold adaptation, mitigates the sensitivity to minor coefficient fluctuations, and keeps thresholds within an optimal range. Furthermore, we integrate the <b>adaptive double-threshold method with a hybrid detection approach</b> combining Energy Detection and <b>Maximum-Minimum Eigenvalue (MME)</b>, which is further fine-tuned using the proposed <b>Innovative Hybrid Whale-Chimp Algorithm</b>. Our approach effectively addresses the limitations of conventional energy detection methods, particularly under low SNR conditions. <b>Collaborative interactions among cognitive nodes</b> enhance detection accuracy, leading to faster spectrum sensing and improved detection probabilities. The proposed method offers a reliable solution for efficient spectrum sensing while safeguarding the integrity of primary users.</p>

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Synergistic Spectrum Sensing: MME and Energy Double Thresholding Enhanced by Hybrid Whale-Chimp Algorithm

  • 期刊论文

  • 发表日期：

    2025年2月1日

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  • A. J. Sharath Kumar,
  • L. R. Raghavendra,
  • R. C. Manjunatha,
  • H. M. Nethravathi

摘要

Spectrum sensing is a critical function in cognitive radio networks, enabling the identification of available frequency bands without interfering with primary users. To improve the effectiveness of energy detection, we propose an adaptive double-threshold method that dynamically adjusts the upper and lower thresholds based on the signal-to-noise ratio (SNR) of cognitive nodes. This research introduces a novel framework for determining the optimal weighting coefficients necessary for these threshold adjustments. Specifically, we present the hybrid Whale-Chimp Optimization Algorithm (WCOA), which ensures stable threshold adaptation, mitigates the sensitivity to minor coefficient fluctuations, and keeps thresholds within an optimal range. Furthermore, we integrate the adaptive double-threshold method with a hybrid detection approach combining Energy Detection and Maximum-Minimum Eigenvalue (MME), which is further fine-tuned using the proposed Innovative Hybrid Whale-Chimp Algorithm. Our approach effectively addresses the limitations of conventional energy detection methods, particularly under low SNR conditions. Collaborative interactions among cognitive nodes enhance detection accuracy, leading to faster spectrum sensing and improved detection probabilities. The proposed method offers a reliable solution for efficient spectrum sensing while safeguarding the integrity of primary users.