## 4 Experimental Results  
### Training Results of the Proposed Models With or Without Pruning (Techniique)  
#### 4.1.1 Experimental Setup for Deep Neural Network Model  
In this study, training was conducted using acoustic signals from a bistatic sonobuoy environment employed in the previous DIFAR sonobuoy signal transmission and reception autoencoder model [6]. The experimental data were generated through bistatic simulations in an underwater environment, and the transmitted signals comprised two types: CW and LFM. The positions of the transmitter and receiver were fixed, and the maximum distance between the target and the sonobuoys was limited to 9 km. The maneuvering range of the target was set between 50 m and 150 m to model the characteristics of reflected signals occurring at various distances.  
The additional conditions for generating simulation data are summarized in Table 2. Signal collection was organized into various scenarios, with each scenario featuring randomly set target positions. The collected data were stored as approximately 13s WAV files. The entire training dataset comprised approximately 86 h of data, and an additional evaluation set of approximately 14 h was used to assess the convergence of model training. The proposed autoencoder model was developed and trained using PyTorch 1.10.1 on an NVIDIA RTX A6000 GPU to handle the extensive dataset efficiently. A ray tracing technique was applied in data generation to reflect underwater acoustic propagation paths, adhering to the same conditions in [6].  
The input signals used for training were normalized to a range of \(-1\) to \(1\) for each file and then segmented into units of 3125 samples, corresponding to 0.1 s. These segmented input data were stacked to facilitate the effective learning of the model for the temporal patterns inherent in underwater acoustic signals during training. The optimizer employed in training was the Adam [27] optimization algorithm, configured to simultaneously update the parameters of the encoder, decoder, and quantizer. The Adam optimizer was initialized with a learning rate of 0.0003 and momentum coefficients \((\beta_{1}.\beta_{2})=(0.5,\ 0.9)\) to enhance training stability and promote rapid convergence, referencing the optimizer parameters from Encode [10]. Furthermore, when the evaluation loss did not improve during the training process, the learning rate was adjusted to finely optimize the performance of the model. To achieve this, a learning rate decay factor of 0.9 was applied, reducing the learning rate by 10% each time the evaluation loss did not decrease. This learning rate adjustment strategy contributed to the stable convergence of the model and the attainment of improved performance. Subsequently, the loss function used for training was structured to enable the model to effectively learn signal reconstruction. The loss function comprised a total of three loss components, which are as follows:  
* Reconstruction Loss: For signal reconstruction loss, the MSE loss function was utilized. This function is employed to minimize the discrepancy between the final output of the model and the input data. MSE is calculated by averaging the squared differences between the input signal and the reconstructed signal.
* Quantization Penalty: This component aims to minimize the error between the quantized vectors generated by RVQ and the original latent vectors. It is defined as the average of the cumulative errors across each quantization layer.
* CM Loss: To minimize errors arising during the quantization process, the loss is defined by comparing the original latent vectors with the output of the CM using MSE. A specific weight (\(\alpha=0.01\)) is multiplied with the compensation loss and added to the overall loss. This weight was set empirically. The final loss function is defined as follows: \[Total\ Loss=Reconstruction\ Loss+Quantization\ Penalty+\alpha\ CM\ Loss.\] (6)