Wide-Range Operation of Microwave Photonic Sensor Using Recurrent Neural Network

In this paper, we present a microwave photonic (MWP) sensor whose operational range and sensing accuracy are enhanced through the utilization of a recurrent neural network (RNN). The MWP sensor utilizes an optical microresonator as the sensor probe and converts the optical resonance responses near the optical carrier frequency into variations in RF transmission with high interrogation resolution. To overcome bandwidth limitations and achieve wide-range operation, a tunable laser is employed to perform the high-resolution interrogation across multiple optical carrier frequencies during each measurement cycle. Subsequently, a RNN, leveraging long-range dependencies and shared parameters, is integrated to process the concatenated interrogation outputs after dimensionality reduction, compensating for output wavelength discrepancies of the tunable laser and enabling accurate wide-range sensing. The proposed approach is experimentally validated using a microring resonator to measure fructose solution concentrations while contending with laser frequency deviation and thermal interference. The operational range of the system is extended three times to 114 GHz, facilitating the measurement of solution concentrations ranging from 49.91% to 30.43% under a temperature variation of 0.61 degrees C and a laser frequency deviation of +/- 2 GHz. The established RNN model demonstrates a root-mean-square error of 0.11%, showcasing 1.60-fold, 2.77-fold, 1.10-fold, and 3.45-fold improvements in accuracy over models based on convolutional neural networks, multilayer perceptrons, sparse vision transformer, and linear fitting, respectively.