Enhancing underwater acoustic orthogonal frequency division multiplexing based channel estimation: a robust convolution-recurrent neural network framework with dynamic signal decomposition

Introduction Underwater acoustic (UWA) communication systems confront significant challenges due to the unique, dynamic, and unpredictable nature of acoustic channels, which are impacted by low signal-to-noise ratio (SNR), severe multipath propagation, latency, Doppler spread, and a shortage of real-world data. Orthogonal frequency division multiplexing (OFDM) is essential for establishing resilient and reliable data transmission in these challenging environments, but accurate channel estimation remains a critical barrier to unlocking its full potential—especially given the limitations of conventional estimation methods in adapting to UWA channel dynamics. Methods This work introduces a Convolution-Recurrent Neural Network (CRNet) estimator integrated with dynamic signal decomposition (DSD) techniques (e.g., Local Mean Decomposition, LMD; Empirical Mode Decomposition, EMD) to estimate UWA-OFDM channel characteristics and mitigate noise-induced distortions in received signals. The CRNet architecture combines convolutional layers (to capture spatial features) and recurrent layers (to model temporal dependencies), enabling it to learn complex UWA channel dynamics. The model is trained using paired data: received pilot symbols, transmitted pilots, and accurate channel impulse responses (CIR). Post-training, CRNet operates using only the received signal as input, eliminating the need for supplementary channel characteristics like SNR. To ensure real-world relevance, training and testing datasets are generated via the Bellhop ray-tracing model, which simulates diverse UWA environments (shallow coastal and continental shelf). Results Numerical findings demonstrate that the proposed CRNet model consistently outperforms benchmark methods—including least squares (LS), minimal mean square error (MMSE), and backpropagation neural network (BPNN)—across key metrics: bit error rate (BER), amplitude error, and phase error. CRNet exhibits superior performance with QPSK modulation compared to QAM, and maintains robustness even with a small number of pilot symbols. Performance evaluations on both training and unseen datasets confirm its resilience and flexibility in demanding UWA environments, validating its ability to generalize to dynamic channel conditions beyond training scenarios. Discussion The CRNet estimator addresses critical limitations of conventional UWA-OFDM channel estimation methods: its dual focus on spatial and temporal features (via convolutional-recurrent layers) overcomes the static linear constraints of LS/MMSE, while DSD-driven noise mitigation enhances input signal quality for more accurate estimation. By eliminating reliance on post-training supplementary channel data (e.g., SNR), CRNet simplifies real-world deployment. Its superior BER performance and adaptability to diverse UWA environments (shallow coastal, continental shelf) position it as a robust solution for improving the reliability and efficiency of UWA communication systems.