## 1 Introduction  
Sonobuoys are disposable devices designed to detect and analyze underwater acoustic signals generated in the ocean. Primarily employed for military purposes, they are used to detect underwater targets, such as enemy submarines, and play a pivotal role in Anti-Submarine Warfare (ASW) [1]. Sonobuoys are typically employed in multi-static detection systems, where multiple sonobuoys operate collaboratively to enhance detection capabilities. In this setup, the transmitter and receiver are positioned separately, providing a wider detection range and improved operational confidentiality compared to monostatic configurations. Notably, the bistatic mode is a simplified version of multi-static detection, involving a single transmitter and receiver operating from separate locations, embodying the basic principles of multi-static systems [2]. This equipment comprises a buoy and an acoustic collection device. The acoustic collection device gathers acoustic signals generated underwater, and the collected signals are transmitted via the buoy to platforms such as Maritime Patrol Aircraft (MPA) [3]. The platform analyzes the received signals to detect and classify underwater targets. Figure 1. illustrates the underwater acoustic collection scenario in a bistatic environment using active and passive sonobuoys in ASW, where the collected acoustic signals are transmitted through wireless communication to the MPA. During the wireless communication process, fading phenomena can occur during signal transmission due to factors such as reflection, refraction, diffraction, and scattering along the propagation path [4]. Fading refers to temporal fluctuations in signal strength and quality, which can adversely impact communication performance. In Figure 1, the schematic visualizes these fading phenomena by depicting a 2-Ray Propagation scenario encompassing both LOS and NLOS conditions. Conventionally, acoustic signals collected by sonobuoys are typically transmitted via wireless communication by modulating the converted signals using Frequency Division Multiplexing (FDM) [5]. However, the FDM approach has several significant limitations. First, a large amount of data transmission is required because the entire acoustic signal is transmitted, which can lead to inefficient consumption of communication resources. Second, multiplexed signals can be relatively easily identified within the frequency band, presenting vulnerabilities in terms of information security [6]. Due to these limitations, relying solely on conventional FDM methods makes it difficult to ensure efficient and secure data transmission between sonobuoys and MPA. Therefore, the introduction of new data transmission methods is required. In this regard, audio compression techniques can be an alternative and various techniques for audio compression have been explored for decades [7; 8; 9; 10; 11; 12; 13; 14]. Traditional audio data compression techniques are broadly categorized into waveform codecs [7] and parametric codecs [8], each focusing on either high-fidelity reconstruction or low bit-rate efficiency. These codecs are designed through signal processing pipelines and meticulous engineering [9]. Recently, advancements in deep learning technologies have led to the introduction of neural network-based approaches in audio codecs, garnering attention for end-to-end codecs that learn complex patterns of input signals and enable high-quality signal reconstruction [9; 10; 11]. These codecs, referred to as neural codecs, extract key features of the signals and perform efficient compression and reconstruction, demonstrating outstanding performance across various types of audio. In particular, a prominent neural codec  
Figure 1: Conceptual diagram of bistatic sonobuoy transmission via wireless communication. is SoundStream [9]. SoundStream employs a quantization technique known as Residual Vector Quantization (RVQ) [9; 10; 12] to compress 24 kHz audio to a minimum of 3 kbps in an end-to-end method, demonstrating high-quality reconstruction performance.