Optimization of the Brillouin instantaneous frequency measurement using convolutional neural networks

The Brillouin instantaneous frequency measurement (B-IFM) is used to measure instantaneous frequencies of an arbitrary signal with high frequency and broad bandwidth. However, the instantaneous frequencies measured using the B-IFM system always suffer from errors, due to system defects. To address this, we adopt a convolutional neural network (CNN) that establishes a function mapping between the measured and nominal instantaneous frequencies to obtain a more accurate instantaneous frequency, thus improving the frequency resolution, system sensitivity, and dynamic range of the B-IFM. Using the proposed CNN-optimized B-IFM system, the average maximum and root mean square errors between the optimized and nominal instantaneous frequencies are less than 26.3 and 15.5 MHz, which is reduced from up to 105.8 and 57.0 MHz. The system sensitivity is increased from 12.1 to 7.8 dBm for the 100 MHz frequency error, and the dynamic range is larger.