Integration of Deep Neural Networks and Local Mean Decomposition for Accurate Underwater Acoustic Channel Estimation

Abstract

Accurate estimation of underwater acoustic channel is crucial for reliable communication in harsh underwater conditions. However, because of the dynamic nature of the underwater acoustic channel, reliable estimation of underwater acoustic channel is difficult. To overcome this problem, we offer a new method for improving UWA channel estimations that combines Deep Neural Networks (DNNs) with Local Mean Decomposition (LMD). To efficiently understand the complicated relationships within the UWA channel data, our proposed technique integrates Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) which captures both spatial and temporal dependencies by using the capabilities of both CNNs and RNN s, allowing for a comprehensive understanding of the UW A channel properties. In addition, we use LMD as a post-processing step at the receiver. LMD enables us to extract meaningful information from noisy signals, improving the accuracy of the estimated underwater acoustic channel. We can limit the negative impacts of noise and interference on received signals by successfully mitigating noise from them, resulting in increased performance. We performed simulations to compare the performance of our proposed model with conventional underwater acoustic channel estimation strategies. Results from simulations show that our approach surpasses these existing algorithms on key parameters including bit error rate (BER) and signal-to-noise ratio (SNR). These results suggest that our technique has a lot of potential for enhancing the reliability and efficiency of underwater acoustic communication systems in complicated underwater environments. The implications of our proposed method are significant, since it allows for improved performance in severe underwater environments. Our method improves the overall reliability of underwater communication systems by giving more precise estimations of the underwater acoustic channel. This remarkable development has far-reaching implications, including greater efficiency, data throughput, and high-quality service in underwater applications such as underwater sensor networks, underwater robotics, and submarine communications.