# Preamble-Based Noncoherent Synchronization in Molecular Communication: A Machine Learning Approach
## 4 Proposed Machine Learning Model  
The transmitted signal passes through the channel, and errors can occur during this process. It is necessary to correct errors, as they can lead to misinterpretation of the received data. ML can be utilized to correct such errors through training [35, 36, 37]. By inputting actual signals, the received signals can be corrected based on the trained data, thus improving accuracy. For ML training, both training data and test data to verify the training results are required, along with setting the number of epochs and learning rate. The more data used for learning, the better the expected outcome can be. However, too much data can take a long time to learn, and can lead to poor learning if noisy or incorrect data are included. Therefore, unnecessary noise and data need to be removed. Epoch can be set in the learning process. Epoch refers to the number of times the entire data has been trained through the model. If there are fewer epochs, they may not be able to identify data patterns due to insufficient learning, and if there are too many epochs, they may learn even the details of the data, resulting in overfitting. Therefore, it is necessary to stop learning at the optimal learning point or to set the optimal epoch value through multiple experiments. However, excessive training iterations can cause overfitting, resulting in increased error rates during testing. The learning rate defines how thoroughly the model learns. A high learning rate can quickly reach the optimal weight values, but may deviate significantly from the optimal point if it is too high. Conversely, a low learning rate may take a long time to find the optimal weights. Therefore, it is essential to use an appropriate learning rate value based on the situation. Figure 4 illustrates the ML model employed for training, consisting of an input layer, hidden layers, and an output layer [38]. The input layer is composed of neurons that utilize probabilistic information about the starting point of the preamble signal and each pulse position as training data. The Softmax activation function is applied in the input layer to transform the input values into a probability distribution, enabling the model to calculate the likelihood of each data point belonging to a specific class. This aids in the training process by clearly distinguishing class distributions. Figure 4: Machine learning model [28]. The hidden layer plays a crucial role in learning complex patterns and relationships within the input data. It contains multiple neurons designed to extract features from the input layer and learn the association between unique data patterns and error signals. To optimize the model's performance, it is essential to determine an appropriate number of layers and neurons. Using too few neurons can lead to underfitting, as the model may fail to capture intricate data patterns. Conversely, using too many neurons can result in overfitting, where the model learns unnecessary details and performs poorly on new data. The Rectified Linear Unit (ReLU) activation function is used in the hidden layers to introduce non-linearity, which allows the model to learn complex data characteristics. ReLU outputs the input value if it is non-negative, otherwise returning zero, effectively filtering out noise and boosting computation speed. The output layer consists of neurons that represent the probability information for each pulse position obtained through learning. The Softmax function is used in the output layer to calculate the probability of the input data belonging to each class, facilitating accurate predictions. To evaluate and optimize the model, the Cross Entropy loss function is employed. Cross Entropy measures the difference between the predicted and actual probability distributions, reacting sensitively to small differences and enhancing the model's prediction accuracy. By minimizing the Cross Entropy loss, the model can effectively detect and correct errors in transmitted signals.