## 5 Discussion  
The results of this study highlight the importance of careful signal selection for QCSK communication systems, particularly when operating under noisy conditions. The results demonstrate that synchronization stability plays a crucial role in signal detection accuracy, directly influencing the BER of the system. The comparison between Vilnius and Colpitts chaos oscillators provides insights into their respective strengths and limitations in chaotic communication systems.  
The Vilnius chaos oscillator with \(v_{\mathrm{C2}}\) as the synchronization signal exhibited the best synchronization robustness, with \(X^{\prime}\) and \(Y^{\prime}\) signals synchronizing at the same level while maintaining low cross-correlation between information-carrying signals. This configuration also proved to be less sensitive to noise, as its correlation values remained stable even under low SNR conditions. These results suggest that this configuration is optimal for chaos-based communication systems, where maintaining synchronization in noisy environments is critical. However, the high cross-correlation between data-carrying signals in this configuration presents a challenge for signal discrimination at the receiver, which may require further refinement in detection methods.  
The Colpitts chaos oscillator with \(v_{\mathrm{C1}}\) as the synchronization signal demonstrated the second-best performance, closely following the Vilnius \(v_{\mathrm{C2}}\) configuration in synchronization stability and BER performance. However, in this case, \(X^{\prime}\) and \(Y^{\prime}\) did not synchronize at equal levels, leading to a system imbalance that required compensation during detection. Despite this, the Colpitts \(v_{\mathrm{C1}}\) configuration maintained the lowest cross-correlation values, suggesting that it may be preferable in applications where minimizing interference between information-carrying signals is a priority.  
In contrast, configurations using \(v_{\mathrm{R1}}\) as the synchronization signal exhibited the weakest performance, particularly in the Vilnius-oscillator-based QCSK system. This configuration was highly sensitive to noise, leading to a significant degradation in synchronization stability and increased BER. In this case, the wider band of the information-carrying signals made them more susceptible to interference, further reducing transmission reliability. These findings emphasize the importance of careful synchronization signal selection, as it directly impacts system robustness against noise and overall transmission performance.  
Furthermore, the results confirm that the imbalance in the synchronization levels negatively affects data detection accuracy, as seen in Table 2. Without a detection threshold, correlation coefficient disparities at higher SNR values led to increased misdetection of '1' and '0' bits, resulting in an uneven bit error distribution. Introducing a threshold-based correction mechanism successfully balanced the system, reducing detection errors and improving BER. These observations underscore the necessity of implementing adaptive compensation techniques in chaos-based communication systems to maintain balanced detection probabilities for reliable data transmission.  
Overall, this study demonstrates that synchronization signal selection and system configuration are critical in determining QCSK performance. The Vilnius \(v_{\text{C2}}\) and Colpitts \(v_{\text{C1}}\) configurations exhibited the most stable synchronization and lower BER, making them strong candidates for secure and efficient chaos-based communication systems. When designing a chaos-based data transmission system, it is essential to evaluate the noise immunity of chaos oscillator synchronization across all possible configurations. Based on the findings of this study, incorporating a threshold into the detector, when necessary, can optimize system performance, leading to an optimal QCSK configuration.