## 1 Introduction  
Due to the applications of underwater acoustic (UWA) channels in both the military and civilian domains, studying their characteristics and performance is immensely important [1]. UWA channels are characterized by severe multipath interference, high Doppler shifts, strong fading, low data rates, and a constrained bandwidth [2; 3; 4]. The probability density function (PDF) of this channel has a broad tail with impulsive behavior which differs from the Gaussian PDF, which makes the t-distribution more convenient to model this channel [5; 6; 7; 8]. Deep learning and modulation techniques with channel coding have been investigated widely to improve the bit error rate (BER) in these channels. A UWA statistical communication channel has been proposed in [9], in which small-scale and large-scale effects of random local displacements were discussed. Large-scale gain and micro-multipath components are included in the model, which produce a complex Gaussian distortion. An efficient computational model for numerical channel simulation was developed through analytical evaluation of the properties of time correlation and frequency correlation. Bessel-type functions describe additional variation from surface motion and displacements. An appropriate model for the total energy, or gain, averaged over small scales is a log-normal distribution. The accuracy of the model in representing the distribution of transmission loss and short-term path gains has been confirmed through validation using actual data from experiments. Logarithmic loss has been used to describe large-scale transmission loss. A new chaotic spread spectrum acoustic communication technique was presented in [10], using a hybrid dynamical system's chaotic signal as a spread spectrum sequence. Multiple propagation and noise have been lessened through the application of a corresponding chaotic matched filter. This technique removed the requirement for intricate channel modulation-demodulation and equalization technologies, in contrast to conventional acoustic communication. The suggested approach achieved a lower BER than some of the other methods that are currently in use, according to the simulation results. Walsh code's orthogonal characteristic was used to combine chaotic reference chips and information-bearing chips in the same time/frequency unit, as shown in [11]. A cyclic-shift interleaver was employed to take advantage of frequency diversity in the chips. The system's BER was analyzed and validated under Gaussian channels and multipath Rayleigh fading channels through simulation. The proposed system's spectral efficiency was compared with other chaos-based communication systems. The study also evaluated BER performance under UWA channels, comparing it with traditional MC direct sequence spread spectrums (SSs) and orthogonal frequency-division multiplexing (OFDM)-based MC-differential chaos shift keying (DCSK) systems. The simulation results confirmed the system's strength under time-varying UWA channels. A brief manual for simulating UWA channels has been provided in [12], emphasizing network modeling. A well-balanced method was presented that combines the ease of automated modeling with systems such as the World Ocean Simulation System (WOSS) with the adaptability of low-level channel modeling through beam tracing. A MATLAB simulation code that interfaces with BELLHOP to produce channel data for underwater acoustic network (UAN) simulations has been included in the tutorial. The research included a case study for each of the two ways to incorporate these data into network simulations: (1) directly import them as a look-up table and (2) use them to build a statistical channel model. The paper's main goal was to provide UAN protocol researchers with a useful modeling tool and learning resource. The statistical channel modeling approach's insights highlight its potential as a reliable tool for upcoming UAN research.