## 2 Related Works
### Enhanced Autoencoder-Based Neural Network Architecture Combining RVQ and CM  
In this study, to efficiently compress and transmit underwater acoustic signals collected by DIFAR sonobuoys, we propose a novel autoencoder-based neural network that integrates RVQ and a CM with the autoencoder architecture in [6]. Figure 4 illustrates the structure of the proposed autoencoder-based system. The proposed model is tailored for the characteristics of sonobuoy signals, which differ significantly from general acoustic signals, such as music or speech. General acoustic signals exhibit complex and dense time-frequency patterns, requiring neural codecs [9; 10; 11; 13; 14] with convolutional neural network (CNN)-based architectures to capture intricate features across the entire signal. Accordingly, general acoustic signal compression and reconstruction models often utilize multi-layer CNNs with strided convolutions to progressively reduce the temporal resolution while extracting features across multiple channels. Although effective for general audio signals, these architectures demand high computational  
Figure 3: Overview of the proposed method: autoencoder-based DIFAR sonobuoy signal transmission and reception systems considering air channel modeling. resources and power, making them unsuitable for environments with limited power availability, such as sonobuoy systems. In contrast, the signals used by sonobuoys, such as Continuous Wave (CW) and Linear Frequency Modulation (LFM), have simple and sparse time-frequency characteristics. CW signals concentrate energy at specific frequencies, while LFM signals exhibit linear frequency changes over time. Leveraging these properties, the proposed model avoids complex CNN layers and instead uses linear layers to process the signals. The input is divided into 0.1-second segments (3125 samples at a sampling frequency of 31,250 Hz), resulting in low-dimensional input due to the short signal length. This enables efficient compression and feature extraction by processing the data in parallel through linear layers. Furthermore, the proposed model integrates RVQ and a CM to enhance compression and reconstruction performance. Unlike general audio codecs that rely on deep and complex architectures, the proposed model is lightweight and tailored to address the power and computational constraints of battery-operated sonobuoy systems. By focusing on the simplicity and sparsity of sonobuoy signals, the proposed model achieves efficient signal processing with a much lower computational load than conventional CNN-based codecs. The proposed model consists of an encoder, quantizer, compensation module, and decoder. First, the encoder compresses the collected acoustic signal into a low-dimensional feature vector. The encoder architecture utilizes multiple linear layers and the ReLU activation function to transform the input signal into a latent vector. While the process is similar to previous approaches, the proposed model differs in that it does not directly reduce the latent space to 10 dimensions. Instead, it first compresses the input to 500 dimensions and then converts it into a 10-dimensional QIV through RVQ. This intermediate step of  
Figure 4: Block diagram of the deep neural network model component in the proposed method. reducing to 500 dimensions offers an advantage over the conventional method [6], which compresses the input directly to 10 dimensions using stacked linear layers. By maintaining a 500-dimensional latent space, the model can preserve critical information in the signal more effectively. RVQ is a multi-stage quantization technique that iteratively reduces residual errors to improve quantization accuracy. Through this process, the 500-dimensional latent vector generated by the encoder is represented as a compressed 10-dimensional QIV. This approach prevents the loss of critical information that may occur in conventional methods [6], where the input signal is directly reduced to 10 dimensions using stacked linear layers, and ensures more stable compression.