## 2 Related Works
### Airchannel Modelling for Realistic Communication Experiments  
To accurately imitate realistic communication environments within the communication system between sonobuoys and MPA, air channel modeling that reflects actual environmental conditions is essential. Extensive research has been conducted on air channel modeling [22; 23; 24], with the free-space path loss and two-ray models being prominent examples. These models offer the advantage of incorporating signal reflections from the ground surface, thereby capturing the effects of signal propagation more effectively.  
Recent studies have extended the traditional two-ray model to develop the Curved-Earth Two-Ray Model [23; 24], which accounts for the curvature of the earth and facilitates precise channel modeling in both terrestrial and aerial communication environments. The Curved-Earth Two-Ray Model was primarily developed to analyze air-ground communications in unmanned aerial systems (UAS). This model assesses the attenuation characteristics of the communication channel by including both the direct path and the ground-reflected path, effectively reflecting path differences due to the curvature of the earth and the conditions of ground reflections. Research in [23; 24] has demonstrated that this model effectively simulates complex phenomena, such as reflection effects caused by path differences over land and water surfaces, thereby providing a more accurate representation of signal behavior in real-world scenarios.  
Proposed Method for Autoencoder-Based DIFAR Sonobuoy Signal Transmission and Reception Systems Considering Air Channel Modeling  
In this paper, we propose a signal compression autoencoder tailored for the transmission and reception environment of DIFAR sonobuoys, along with an air channel model communication system to validate its effectiveness. The overall system flow is as follows. As illustrated in Figure 3, the sonobuoy collects underwater acoustic signals, which are then converted into QIV (Quantized Index Vectors) through an encoder mounted on the sonobuoy. These vectors are subsequently transformed into bit-streams and modulated into signals suitable for wireless communication using GMSK (Gaussian low-pass-filtered Minimum Shift Keying) digital modulation. The modulated signals are transmitted to an MPA, where the received GMSK signals are demodulated to restore the bitstreams back into QIV. Then QIV are processed by a decoder to reconstruct the original acoustic signals.  
Performance improvement of the proposed system is influenced by the improved design of the deep learning model, which will be discussed in detail in Section 3.1. Additionally, since sonobuoys are battery-operated devices, minimizing computational load and reducing the number of parameters is essential to overcome operational time constraints. Consequently, the encoder of the proposed system has been optimized using pruning techniques, and the optimization algorithms and specific methodologies employed are elaborated in Section 3.2. Furthermore, considering the communication environment between sonobuoys and MPA, we have designed an air channel model that accurately reflects the signal characteristics during actual wireless transmission. Detailed aspects of the air channel model design are addressed in Section 3.3.