Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.
PLoS One. 2021 Nov 23;16(11):e0259955. doi: 10.1371/journal.pone.0259955. eCollection 2021.
Light fidelity (LiFi) uses different forms of orthogonal frequency division multiplexing (OFDM), including DC biased optical OFDM (DCO-OFDM). In DCO-OFDM, the use of a large DC bias causes optical power inefficiency, while a small bias leads to higher clipping noise. Hence, finding an appropriate DC bias level for DCO-OFDM is important. This paper applies machine learning (ML) algorithms to find optimum DC-bias value for DCO-OFDM based LiFi systems. For this, a dataset is generated for DCO-OFDM using MATLAB tool. Next, ML algorithms are applied using Python programming language. ML is used to find the important attributes of DCO-OFDM that influence the optimum DC bias. It is shown here that the optimum DC bias is a function of several factors including, the minimum, the standard deviation, and the maximum value of the bipolar OFDM signal, and the constellation size. Next, linear and polynomial regression algorithms are successfully applied to predict the optimum DC bias value. Results show that polynomial regression of order 2 can predict the optimum DC bias value with a coefficient of determination of 96.77% which confirms the effectiveness of the prediction.
光保真度（LiFi）使用不同形式的正交频分复用（OFDM），包括偏置直流光 OFDM（DCO-OFDM）。在 DCO-OFDM 中，大的直流偏置会导致光功率效率低下，而小的偏置会导致更高的削波噪声。因此，为 DCO-OFDM 找到合适的直流偏置电平很重要。本文应用机器学习（ML）算法为基于 LiFi 系统的 DCO-OFDM 找到最佳直流偏置值。为此，使用 MATLAB 工具为 DCO-OFDM 生成一个数据集。然后，使用 Python 编程语言应用 ML 算法。ML 用于找到影响最佳直流偏置的 DCO-OFDM 的重要属性。结果表明，最佳直流偏置是几个因素的函数，包括双极性 OFDM 信号的最小值、标准差和最大值，以及星座大小。接下来，成功地应用线性和多项式回归算法来预测最佳直流偏置值。结果表明，阶数为 2 的多项式回归可以以 96.77%的确定系数预测最佳直流偏置值，这证实了预测的有效性。
