Gain Control Method for FRA based on Neural Network and Numerical Solution of Equations

【Objective】Fiber Raman Amplifier (FRA) based on multi-pump technology has features of low noise, a wide gain bandwidth, and a controllable gain spectrum shape, which is regarded as an ideal optical relay amplifier for long-haul fiber optic transmission network systems. Intelligent optical amplifiers with adaptive controllable gain are required in dynamic fiber optic transmission network systems. This article introduces a gain control method for FRA based on neural network and numerical solutions of Raman power coupling equations.【Methods】First, the data set containing the signal gains, pump powers and wavelengths in the FRA is collected to train the neural network to establish an approximate mapping relationship between the signal gains and pump parameters. Subsequently, the trained neural network is utilized to determine the initial pump powers and wavelengths of the FRA based on the target gains of the signal. Finally, the pump powers are optimized by solving the numerical solutions of the Raman power coupling equations to improve the accuracy of the FRA output signal gains.【Results】The paper investigates the effect of the flatness of signal gains in each group in the training dataset on the accuracy of FRA output signal gains. When the gain fluctuation of each group signal in the training data is less than 2 dB, the mean and variance of the Root Mean Square Error (RMSE) of the 1 000 sets of test signal gains output by the FRA are 0.230 and 0.010 dB, respectively. Additionally, the mean and variance of the maximum error of the gains are 0.462 and 0.044 dB, respectively.【Conclusion】The results indicate that the proposed method can achieve high-precision FRA gain control, offering a new idea and method for investigating intelligent optical amplifier gain adaptive control in dynamic fiber optic transmission networks.