Optimum DC bias for clipping distortion mitigation in DCO-OFDM

This dataset accompanies a research paper on leveraging Machine Learning (ML) techniques for regression to predict the optimum DC bias in direct current in optical orthogonal frequency division multiplexing (DCO-OFDM).  The dataset comprises a set of features to facilitate the prediction of the required DC bias to mitigate the impact of clipping distortion at the transmitter. MATLAB software was utilized for modelling the DCO-OFDM transmission and generating the research dataset. The dataset included some characteristic features such as number of subcarriers (N), constellation size (M), and peak-to-average power ratio (PAPR), and some signal statistical features such as minimum (Min), maximum (Max), standard deviation (std), skewness, and kurtosis. Other feature columns such as DC bias, scaling factor, and bit error rate are also included to employ the regression model to optimize the DC bias for a threshold transmission error impacted by clipping. The generated dataset comprises 3500 samples of the transmitted signals across various transmission scenarios.