Neural Network-based Subcarrier-level Joint Channel Estimation and Decoding for MIMO-OFDM Receivers

The increasing demands of modern telecommunications require improvements in spectral efficiency and system throughput. In this context, our study introduces a novel decoding method for MIMO-OFDM systems employing parallel neural networks, which markedly enhances decoding speed and accuracy over previous models. Unlike serial decoding, which fails to address the unique characteristics of individual subcarriers, our method employs distinct phase-transmittance radial basis function (PT-RBF) neural networks for each subcarrier. This parallel processing approach significantly reduces decoding time and increases system adaptability by effectively managing nonlinear impairments and intersymbol interference. Simulation results show that our method outperforms conventional decoding techniques in reducing bit error rate (BER) across both linear and nonlinear scenarios.