The performance of underwater acoustic (UWA) communication is heavily dependent on channel estimation, which is predominantly researched by simulating UWA channels modelled in complex and dynamic underwater environments. In UWA channels modelling, the measurement-based approach provides an accurate method. However, acquirement of environment data and simulation processes are scenario-specific and thus not cost-effective. To overcome such restraints, this article proposes a comprehensive simulation platform that combines UWA channel modelling with orthogonal frequency division multiplexing (OFDM) channel estimation, allowing users to model UWA channels for different ocean environments and simulate channel estimation with configurable input parameters. Based on the simulation platform, three independent simulations are conducted to determine the impacts of receiving depth, sea bottom boundary, and sea surface boundary on channel estimation. The simulations show that UWA channel estimation is greatly affected by underwater environments. The effect can be mainly attributed to changing acoustic rays tracing which result in fluctuating time delay and amplitude. With 10 m receiving depth and flat sea bottom, the channel estimation achieves optimal performance. Further study indicates that the sea surface has stochastic effects on channel estimation. As the significant wave height (SWH) increases, the average performance of channel estimation shows improvements.

For underwater acoustic communication, pilot pattern optimization is usually investigated to improve the performance of channel estimation based on compressed sensing (CS) in orthogonal frequency division multiplexing (OFDM) systems. However, there is no deterministic criteria to design a perfect pilot pattern utilizing the measurement matrix, and no mature methods to quantitatively measure the relationship between the influence indicators and estimation performance of pilot patterns. An analytical method with grey correlation degree is proposed to try to solve the problem. The influence indicators are weighted with information entropy and the grey correlation degrees of various optimization strategies are calculated. Experimental results demonstrate the proposed method is intuitive and effective, due to the order of the grey correlation degrees entirely consists with the order of the channel estimation performance on bit error rate (BER) and mean square error (MSE). Moreover, it is indicated that the ratio of large off-diagonal entries in the Gram matrix has a greater impact on the performance of channel estimation compared to the minimal mutual coherence, the ratio of small off-diagonal entries, and the ratio of middle off-diagonal entries.