Dataset_Optica.zip

The terahertz communications band in the 252 to325 GHz range has been recently explored for its potential to meet the stringent requirements for the emerging sixth generation of wireless communications. However, there are several challenges including noise and nonlinearity that hinder efficient implementations. We aim to address this limitation in terahertz communications through convolutional neural networks (CNN) enhanced by the domain knowledge from traditional Volterra filters. This dataset contains the time-domain data used to train a CNN model used for nonlinearity compensation in a terahertz communications system. The proposed model achieves a 2.28 dB improvement