Optical frequency multiplication using residual network with random forest regression

• DOI: 10.1016/j.heliyon.2024.e30958
• Publication Date: 2024-05-16T07:57:27Z
• Authors: Qi Zhang; Xu Han; Xinyu Fang; Ming Liu; Ke Ge; Hong Jiang
• Keywords: Social sciences (General); H1-99; Q1-390; Science (General); Random forest regression; Frequency multiplication modulation; 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; Deep learning algorithm; Residual network; Research Article

In this work, we present a method for optical frequency multiplication utilizing hybrid deep learning approach that integrates the Residual Network (ResNet) with Random Forest Regression (RFR) algorithm. Three different modulation schemes are adopted to illustrate method, which can obtain suitable parameters these schemes. Based on predicted by algorithm, 8-tupling, 12-tupling, and 16-tupling mm-wave signals generated numerical simulation. The simulation results show 8-tupling multiplication, an OSSR (optical sideband suppression ratio) is 30.73 dB RFSSR (radio spurious of 80 GHz 42.29 dB. For 12-tupling 30.09 dB, 120 mm wave 36.21 generating mm-wave, 29.86 34.52 obtained. addition, impact amplitude fluctuation bias voltage drift quality also studied.