# Preamble-Based Noncoherent Synchronization in Molecular Communication: A Machine Learning Approach
## 6 Conclusions  
MC transmits information using molecules, and holds potential applications in various fields, such as medicine and chemistry. However, implementing complex communication procedures in restricted environment like the human body poses challenges for MC. Synchronization is still an open challenge in CEMC. This paper introduces a preamble-based synchronization technique, and proposes an additional method to enhance synchronization performance using ML. The proposed preamble-based synchronization technique has a simple structure, resulting in low computational complexity, and utilizes a molecular concentration comparison method. By measuring the concentration of molecules in each segment and selecting the maximum concentration among the LCWs, the synchronization start point can be set. This method achieves better synchronization performance in the slow transmission environment characteristic of MC channels. Additionally, the LCW data obtained are used to train ML models to further enhance synchronization accuracy. Simulation results of the proposed method showed that \(P_{\text{sync}}\) was close to 1 when SCW width was 8 \(T_{p}\), and \(\text{NMSE}_{\text{avg}}\) showed performance of about 0.02. \(P_{\text{sync}}\) showed performance of up to 0.8 for \(4T_{p}\), \(\text{NMSE}_{\text{avg}}\) showed performance of up to 0.08 for \(2T_{p}\), and \(\text{NMSE}_{\text{avg}}\) showed performance of 0.02. Simulation experiments verified the synchronization probability and performance of the preamble signal. It was observed that increasing the size of the SIW segments reduced synchronization errors. Furthermore, synchronization performance varied depending on the chosen LCW size. Therefore, adjusting these parameters appropriately in MC is expected to improve the performance of preamble-based synchronization. Overall, as the transmission distance increased, synchronization performance deteriorated. This is due to the increased delay phenomena that occur with longer transmission times, characteristic of the channel. Moreover, it was confirmed that ML training can improve synchronization performance. This is because it learns the characteristics of the channel and compensates for errors caused by delays, thereby enhancing accuracy. The proposed preamble-based synchronization method and the approach to improve synchronization performance through ML are promising due to their low complexity and simple design requirements, given the channel characteristics of MC. Further studies will apply and compare the proposed methods in various models, and deal with multi-path problems and the advancement of ML models to improve synchronization performance.  
Figure 9: Average normalized mean square error with distance for different SCW widths after training.  
**Author Contributions**: Conceptualization, S.-Y.J.; methodology, S.-H.M. and S.-Y.J.; software, S.-H.M.; validation, S.-H.M., P.S. and S.-Y.J.; formal analysis, P.S. and S.-Y.J.; investigation, S.-H.M. and P.S.; resources, S.-Y.J.; data curation, S.-H.M.; writing--original draft preparation, S.-H.M. and P.S.; writing--review and editing, P.S. and S.-Y.J.; visualization, P.S. and S.-Y.J.; supervision, S.-Y.J.; project administration, P.S. and S.-Y.J.; funding acquisition, P.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by NATIONAL RESEARCH FOUNDATION OF KOREA (NRF) grant number 2022R1G1A1004799.  
Not applicable.  
**Data Availability Statement**: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.