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Spectrum Sensing Algorithm by Multi-Objective Optimization Theory and Fuzzy Integral Method
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DOI：10.1109/ACCESS.2023.3319368.png)
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Abstract
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As spectrum resources become increasingly scarce, efficient spectrum management is crucial in wireless communication. To address this challenge, a new spectrum sensing algorithm based on multi-objective optimization theory and fuzzy integral method has been developed. This innovative algorithm combines the whale algorithm and fuzzy integral algorithm, allowing for multi-objective optimization and interference decision-making. It overcomes limitations in existing multi-user spectrum sensing methods, such as low perception probability, poor detection accuracy, high false alarm probability, and computational complexity. Comparative studies show that the multi-objective whale algorithm achieves excellent optimization performance and fast convergence, reaching a throughput of 16.8 after 600 cycles. The collaborative spectrum sensing method, especially the one based on fuzzy integration, performs exceptionally well, particularly in high signal-to-noise ratio environments. This algorithm has significant application potential and effectively addresses spectrum resource management in wireless communication. It offers benefits such as improved spectrum utilization efficiency, scalability, and practicality, making valuable contributions to technological progress and efficiency enhancement in the field.
Keywords:
Whale algorithm
fuzzy integral method
spectrum sensing
multi objective optimization theory
interference decision
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