Linear Regression vs. Deep Learning for Signal Quality Monitoring in Coherent Optical Systems

Abstract

Error vector magnitude (EVM) is a metric for assessing the quality of m-ary quadrature amplitude modulation (mQAM) signals. Recently proposed deep learning techniques, e.g., feedforward neural networks (FFNNs) -based EVM estimation scheme leverage fast signal quality monitoring in coherent optical communication systems. Such a scheme estimates EVM from amplitude histograms (AHs) of short signal sequences captured before carrier phase recovery (CPR). In this work, we explore further complexity reduction by proposing a simple linear regression (LR) -based EVM monitoring method. We systematically compare the performance of the proposed method with the FFNN-based scheme and demonstrate its capability to infer EVM from an AH when the modulation format information is known in advance. We perform both simulation and experiment to show that the LR-based EVM estimation method achieves a comparable accuracy as the FFNN-based scheme. The technique can be embedded with modulation format identification modules to provide comprehensive signal information. Therefore, this

Low-complexity EVM estimation based on artificial neural networks for coherent optical systems

“…The proposed ANN reports an overall MAE of 0.0432%, indicating a notable improvement in accuracy. When compared to the MAE published by [11,13,19] which varies between 0.06% and 3.7% for various modulation forms, this figure stands out significantly. The measured MAE points to both a significant improvement and a possible breakthrough in coherent optical communications EVM estimation accuracy.…”

“…The structural arrangement of the model is tested and refined for optimum performance. After that, a loss function [18][19][20][21] is employed to quantify the discrepancy between the real reference value and the anticipated output. The derivative of the loss is analyzed so that the influence of each contribution of the weight to the overall loss can be evaluated.…”

Multi-Class Classification and Multi-Output Regression of Three-Dimensional Objects Using Artificial Intelligence Applied to Digital Holographic Information

“…Further, the MLP regressor had a better performance on the test/validation sets for the hologram and phase-only image datasets with a fixed EV regression score of 0.00 compared to the CNN and the other machine learning regressors [ 51 ]. The RF regressor had a better performance on the validation set for the concatenated intensity–phase image dataset with a stable EV regression score of 0.01 compared to the CNN and the other regressors [ 52 ]. Therefore, we concluded that both the CNN and the machine learning classifiers and regressors (KNN, MLP, DT, RF, and ET) had a superior performance in both the five-class classification and regression tasks for all three datasets.…”