Spectrum-Weighted Fusion Cooperative Detection Algorithm Based on Double Thresholds for Underwater Acoustic Networks

Area of Science:

• Underwater acoustic communications
• Cognitive radio technology
• Signal processing

Background:

• Spectrum sensing is vital for underwater acoustic communication networks, enabling detection of spectrum holes and channel occupancy.
• Energy detection is a fundamental spectrum sensing technique in cognitive radio, but traditional methods face challenges in complex hydroacoustic channels due to fixed thresholds.
• Hydroacoustic channel complexity necessitates adaptive solutions for effective spectrum sensing.

Purpose of the Study:

• To develop an improved spectrum sensing technique for hydroacoustic communication systems.
• To address the limitations of traditional double-threshold energy detection in complex underwater environments.
• To enhance the utilization of the hydroacoustic spectrum through adaptive thresholding.

Main Methods:

• Proposed a hydroacoustic channel model incorporating a signal-to-noise ratio (SNR) based weight factor.
• Developed an adaptive thresholding algorithm adjusting to the user's SNR environment.
• Utilized simulation experiments to compare the proposed technique with existing spectrum sensing methods.

Main Results:

• The proposed SNR-weighted collaborative spectrum-sensing technique demonstrated superior detection performance.
• Adaptive thresholding significantly reduced false detection rates compared to traditional methods.
• The technique showed enhanced overall detection performance in simulated hydroacoustic environments.

Conclusions:

• The SNR-weighted collaborative spectrum-sensing technique offers a more flexible and accurate approach for underwater acoustic networks.
• Adaptive thresholding based on SNR is effective in overcoming the limitations of fixed thresholds in complex hydroacoustic channels.
• This advancement can significantly improve the efficiency and reliability of underwater communication systems.