## 1 Introduction  
In recent years, underwater acoustic (UWA) communication has been widely employed in military affairs [1], ocean exploration [2], pollution monitoring [3], offshore oil drilling [4], etc. In view of these applications, UWA communication technology shows great potential as an area of research. Nevertheless, the nature of UWA channel (such as low available bandwidth, time-varying multipath, and the speed of sound) hinders the efficiency of communication devices [5]. Orthogonal frequency division multiplexing (OFDM), a robust method of encoding digital data on multiple carrier frequencies, is generally used in UWA time-dispersive channels and has the ability to render inter-symbol interference (ISI) negligible by embedding a cyclic prefix [6]. Furthermore, in order to suppress inter-channel interference (ICI) [7] introduced by Doppler spread in the OFDM system, the knowledge of channel impulse response (CIR) is essential, which could be acquired with the help of pilot signals [8]. Therefore, the estimation of channel is the key factor in the performance of UWA communication. Although the estimation of UWA channel still remains a sophisticated problem due to complex underwater environments, channel modelling is an efficient way to perform preliminary evaluation and decide parameters such as estimation algorithm, pilot interval, and number of carriers for the OFDM communication device [9]. UWA channel modelling can be classified to two generic approaches: geometry-based and measurement-based [10]. Geometry-based UWA channel modelling relies on mathematical analysis in physical characteristics. [PERSON] [11] proposed a geometry-based model for multiple-input multiple-output (MIMO) mobile-to-mobile (M-to-M) shallow UWA channel by taking both macro- and microscattering effects into account. [PERSON] and [PERSON] [12] developed a statistical model of UWA channels that incorporates physical aspects of acoustic propagation with the effects of inevitable random channel variations. [PERSON] et al. [13] constructed a stochastic channel model for wideband single-input single-output (SISO) shallow UWA channels under the assumption of rough ocean surface and bottom. These geometry-based models generally display stochastic channel responses in consideration of the time-varying characteristic of UWA channels collected through measurement campaigns. Although geometry-based channels could achieve a high level of accuracy, the physical features of the channels are limited in specific cases. For example, the probability distribution functions (PDF) of UWA channel envelope was revealed to be Rice distribution in [14], Rayleigh distribution in [15], log-normal distribution in [16], and K-distribution in [17]. The differential distributions above imply that the precise UWA channels should be modelled with real environment data. BELLHOP [18; 19] is a beam tracing model that considers ocean environment properties data, such as sea surface boundary, sea bottom boundary, and sound speed profile (SSP). Utilizing the precision of BELLHOP, numerous measurement-based UWA channels were built through the model. In [20], [PERSON] et al. compared real UWA channel data measured from an experiment with the channel obtained through the BELLHOP model. The result showed a great agreement in the conditions of calm ocean, which confirmed the feasibility of BELLHOP model. With strong winds, the measured channels were slightly worse than the result of the BELLHOP model. The deviation was caused by the lack of consideration on the sea surface. It also revealed the limitation of the BELLHOP model when dealing with stochastic parameters. In [21; 22; 23], shallow UWA channels were modelled in the eastern shore of Johor and the Taiwan Strait through the BELLHOP model and environment data were sourced from various databases. These approaches of measurement-based channel modelling provided an accurate way to analyse the channel characteristics such as transmission loss, CIR and ray tracing.