Computing Descriptive Statistics of Bluetooth Signals Using a Neural Network

This paper presents a novel approach to analyzing Bluetooth signals using deep learning with neural networks. Due to their complex properties, Bluetooth signals require advanced methods for analysis. Thes neural network model predicts the mean and standard deviation of these signals, utilizing fully connected layers and Tanh and Softmax activation functions. Model performance is evaluated with metrics such as MAE, MSE, RMSE, and R-squared. The research demonstrates that neural networks can accurately extract statistical features from Bluetooth signals, contributing to improvements in wireless communication by optimizing signal analysis and error detection.