Analysis for SNR Reduction in Underwater Acoustic Communication Using Real and Complex Wavelets

Underwater acoustic communication is vital for applications such as environmental monitoring, naval operations, and autonomous underwater vehicle networks. However, reliable data transmission underwater is severely impacted by
ambient noise, originating from natural sources like surface waves and biological activity, as well as human activities such as shipping and construction. Effective noise reduction techniques are necessary to enhance signal quality. This study evaluates the performance of both real and complex wavelets in de-noising underwater acoustic signals affected by real wind noise. The sample noise data was collected from the acoustic group, National Institute of Ocean Technology (NIOT), covering frequencies from 100 Hz to 20 kHz. An input signal, modulated using a rectangular carrier, is used as the test signal. The noisy signal is processed using wavelet transform-based de-noising, with symlet and bi-orthogonal wavelets representing real wavelets, and
gabor and bump wavelets representing complex wavelets. Performance is evaluated by comparing input and output signal-to-noise ratios (SNR).Results show that complex wavelets consistently outperform real wavelets in suppressing noise. The gabor wavelet achieves the highest output SNR of 17.54 dB for an input SNR of − 15.87 dB. Among real wavelets, the symlet wavelet provides a peak output SNR
of 14.58 dB. This analysis highlights the effectiveness of complex wavelets for improving underwater acoustic communication. Future work will explore hybrid denoising approaches, combining wavelets with adaptive filtering techniques to further enhance signal quality in noisy underwater environments