## 4 Experimental Results
### Assessing the Corruption Impact of QIV in a Wireless Communication Environment
#### 4.2.2 Experimental Results of Air Channel Model  
The experimental setup modeled the channel characteristics by reflecting the dynamic positional changes of the sonobuoy and the aircraft. To validate this, the path loss of the air channel model was measured. The results of the path loss calculation are shown in Figure 9, illustrating the changes in path loss (dB) relative to the X-axis distance. The graph reflects dynamic variations caused by positional changes of the sonobuoy and the aircraft, showing minimal loss at closer distances and a gradual increase in loss as the distance grows. This validation confirmed the accuracy of the channel model and its suitability for BER measurements under realistic communication conditions. To evaluate the degradation of the Quantized Index Vector under various conditions, this study measured the BER in an AWGN channel across different SNR conditions and analyzed its impact on the reconstruction performance of the neural codec. Quantitative evaluation metrics, including Spectrogram MSE, LSD, and SNR, were measured between the clean sonobuoy-collected signals and the reconstructed signals. The results are presented in Table 7. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Signal** & **AWGN** & **BER** & **Spectrogram** & **LSD** & **SNR** \\  & **Type** & **SNR** & **(\%)** & **MSE** & **(dB)** & **(dB)** \\ \hline \multirow{2}{*}{Conventional} & CW & & 0.00006 & 0.00277 & 1.19 & 25.93 \\  & LFM & & 0.00006 & 0.01428 & 1.19 & 17.66 \\ \hline \multirow{2}{*}{Proposed} & CW & & 0.00010 & 0.00113 & 1.12 & 29.19 \\  & LFM & & 0.00010 & 0.00644 & 1.10 & 21.56 \\ \hline \multirow{2}{*}{Proposed–Pruned} & CW & & 0 & 0.00120 & 1.15 & 28.59 \\  & LFM & & 0 & 0.00719 & 1.12 & 21.08 \\ \hline \multirow{2}{*}{Conventional} & CW & & 0.00019 & 0.00277 & 1.19 & 25.93 \\  & LFM & & 0.00012 & 0.00142 & 1.19 & 17.66 \\ \hline \multirow{2}{*}{Proposed} & CW & & 0.00040 & 0.00129 & 1.13 & 29.03 \\  & LFM & & 0.00040 & 0.00649 & 1.10 & 21.54 \\ \hline \multirow{2}{*}{Proposed–Pruned} & CW & & 0.00020 & 0.00123 & 1.15 & 28.54 \\  & LFM & & 0.00040 & 0.00723 & 1.12 & 21.07 \\ \hline \multirow{2}{*}{Conventional} & CW & & 0.00192 & 0.00277 & 1.19 & 25.93 \\  & LFM & & 0.00154 & 0.02075 & 1.19 & 21.22 \\ \hline \multirow{2}{*}{Proposed} & CW & & 0.00215 & 0.00209 & 1.13 & 29.14 \\  & LFM & & 0.00225 & 0.00751 & 1.10 & 21.53 \\ \hline \multirow{2}{*}{Proposed–Pruned} & CW & & 0.00225 & 0.00304 & 1.15 & 28.55 \\  & LFM & & 0.00266 & 0.00939 & 1.12 & 21.07 \\ \hline \multirow{2}{*}{Conventional} & CW & & 0.01126 & 0.04241 & 1.19 & 24.48 \\  & LFM & & 0.01120 & 0.23350 & 1.19 & 16.67 \\ \hline \multirow{2}{*}{Proposed} & CW & & 0.01207 & 0.01681 & 1.14 & 25.28 \\  & LFM & & 0.01125 & 0.01856 & 1.11 & 19.68 \\ \hline \multirow{2}{*}{Proposed–Pruned} & CW & & 0.01135 & 0.00765 & 1.16 & 27.83 \\  & LFM & & 0.01156 & 0.01600 & 1.13 & 19.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparison of autoencoder models under AWGN channel with various SNR. Figure 9: Path Loss of 2-ray propagation air channel model. Table 7 provides a detailed comparison of the performance of the proposed neural network and its pruned version with the existing DIFAR sonobuoy-based signal compress transmission and reception neural network under various SNR conditions in the AWGN channel. The results demonstrate that the proposed method quantized the latent vector, originally represented as a 32-bit floating-point value, into a 10-bit integer using the RVQ technique, reducing the transmission size to approximately 37.5% compared to the 16-bit integer quantization of the existing method. While this enables efficient data transmission in constrained communication environments with reduced transmission amount of data, it also introduces greater sensitivity to BER. The experiments revealed that the proposed method exhibited relatively higher BER under various SNR conditions in the AWGN channel.