Interpolated Polynomial based Black Widow Optimization Scheme for Channel Estimation in Underwater Acoustic Communication

The proposed study introduces an Optimized Least Square Sparse (OLSS) Channel Estimation (CE) algorithm for Underwater Acoustic (UWA) communication using Orthogonal Frequency Division Multiplexing (OFDM). Traditional CE methods encounter challenges like higher complexity and slower convergence, impacting performance estimation accuracy. The OLSS algorithm addresses these limitations by integrating the least square (LS) CE algorithm through an Interpolated Polynomial based Black Widow Optimization (IBWO). The algorithm utilizes 2's complement shift left turbo encoding (2CSL-TE) for signal encoding, followed by inverse fast Fourier transform (IFFT) operations and trans-mission through the UWA channel. Notably, OLSS optimizes sparse CE in the interference-free region of the received OFDM signal. Evaluation metrics, including Symbol Error Rate (SER) of 0.000000463, Bit Error Rate (BER) of 0.048, Mean Square Error (MSE) of 0.101, and Peak Signal-to-Noise Ratio (PSNR) of 22.62, demonstrate the OLSS algorithm superior when compared to traditional methodologies. The OLSS algorithm proves effective in enhancing accuracy and convergence speed, positioning it as a robust solution for UWA-OFDM communication systems.