# Preamble-Based Noncoherent Synchronization in Molecular Communication: A Machine Learning Approach
###### Abstract  
In the field of wireless communication, there is growing interest in molecular communication (MC), which integrates nano-, bio-, and communication technologies. Inspired by nature, MC uses molecules to transmit data, especially in environments where EM waves struggle to penetrate. In MC, signals can be distinguished based on molecular concentration, known as concentrated-encoded molecular communication (CEMC). These molecules diffuse through an MC channel and are received via ligand-receptor binding mechanisms. Synchronization in CEMC is critical for minimizing errors and enhancing communication performance. This study introduces a novel preamble-based noncoherent synchronization method, specifically designed for resource-constrained environments like nanonetworks. The method's simple, low-complexity structure makes it suitable for nanomachines, while machine learning (ML) techniques are used to improve synchronization accuracy by adapting to the nonlinear characteristics of the channel. The proposed approach leverages ML to achieve robust performance. Simulation results demonstrate a synchronization probability of 0.8 for a transmitter-receiver distance of 1 cm, given a molecular collection time duration four times the pulse duration. These results confirm the significant benefits of integrating ML, showcasing improved synchronization probability and reduced mean square error. The findings contribute to the advancement of efficient and practical MC systems, offering insights into synchronization and error reduction in complex environments. preamble; synchronization; molecular communication; machine learning; noncoherent accumulation; ligand-receptor binding +
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channels leads to the accumulation of residual molecules, resulting in inter-symbol interference (ISI) [8]. Consequently, accurate data transmission requires a precise understanding of channel characteristics and the development of methods to mitigate ISI. Advancements in biology and nanotechnology have enabled the engineering of cellular functions into nanomachines. Given the limited environment and performance capabilities of nanomachines, a synchronization method that is simple, practical, and low in complexity is essential for accurate signal transmission [9]. Various synchronization techniques, such as correlation-based [10; 11] and least-squares-based methods [12], have been proposed in the literature. In [13], a blind synchronization method was introduced to address the communication overhead caused by the low throughput of MC systems. To improve synchronization accuracy in nanomachines, a bidirectional message exchange mechanism model based on the inverse Gaussian distribution was proposed [14]. To address the randomness arising during the molecule generation and emission processes in the constrained environment, a method employing multiple types of molecules for symbol synchronization and data detection has been developed [15]. In [16], a maximum likelihood estimator for time offset in an MC system with drift was proposed, while in [17], a reference broadcast synchronization method was introduced to account for molecular synthesis time. Additionally, a preamble-based in synchronization method was suggested in [18; 19]. A preamble refers to a specific signal inserted before the transmitted data. By sending a simple preamble signal that is easily distinguishable from the data and measurable prior to data transmission, the timing of the preamble signal can be estimated, facilitating synchronization and subsequent data transmission. Depending on the design and measurement method of the preamble signal, synchronization process can be significantly simplified. The preamble-based synchronization method proposed in [18; 19] features a simple design structure and low computational complexity, making it particularly suitable for nanonetworks. In this study, we adopt the proposed preamble-based synchronization method [18; 19] in the MC environment to evaluate synchronization probability and timing accuracy.