Advanced Symbol Detection in OFDM Systems Using VGAL-Net: A Comprehensive Deep Learning Framework

The VGAL-Net model, presented in this study, is a deep learning-based method specifically developed to improve the accuracy of symbol detection in Pilot-Aided Orthogonal Frequency Division Multiplexing (OFDM) systems. The model employs Long Short-Term Memory (LSTM) networks to capture temporal dependencies, thereby enhancing the detection and decoding of transmitted symbols. The methodology entails creating training data by simulating OFDM systems using Offset Quadrature Phase Shift Keying (QPSK) modulation, including fixed pilot symbols, and conducting channel estimation. VGAL-Net’s performance is assessed by comparing it to traditional methods like Least Squares (LS) and Minimum Mean Square Error (MMSE) across different Signal-to-Noise Ratio (SNR) scenarios. The results indicate that VGAL-Net exhibits exceptional accuracy, as evidenced by its rapid convergence, low Symbol Error Rates (SER), and consistent performance in various operational scenarios. These findings underscore its potential in advancing wireless communication technologies.