The optical signal-to-noise ratio (OSNR) and fiber nonlinearity are critical factors in evaluating the performance of high-speed optical fiber communication systems. Recently, several deep learning based methods have been put forward to monitor OSNR of a fiber communication system. In this work, we propose a long short-term memory (LSTM) network based method to simultaneously estimate OSNR and nonlinear noise power caused by fiber nonlinearity. In the training step, LSTM network extracts the essential features in frequency domain of the input signal. Then, with the built model in the training step, the LSTM output the OSNR and nonlinear noise power of the signal under test. The simulation by VPI software is carried on a 5-channel long haul optical transmission system with the launched optical power of -3.0~ + 3.0dBm per channel. The results show that the test error of OSNR is less than 1.0dB with the reference OSNR from 15 to 30dB for QPSK, 16QAM and 64QAM signal. The test error of nonlinear noise power is less than 1.0dB for QPSK and 16QAM signal when the Laser linewidth is 6 KHz and 100 KHz respectively. The proposed method is a promising candidate for nonlinearity-insensitive OSNR and accurate nonlinear noise power estimation in multi-channel long haul optical fiber communication systems.
In this letter, we propose an indoor visible light positioning technique that combines deep neural network based on the Bayesian Regularization (BR-DNN) with sparse diagonal training data set. Unlike other neural networks, which require a large number of training data points to locate accurately, we realize the high precision positioning with only 20 training points in a 1.8 m × 1.8 m × 2.1 m location area. Furthermore, we test a new optimization method of training data set, which is the diagonal set. To verify our ideas, we experimentally demonstrate three different training data acquisition methods that contain the common choice of training points (even set), arbitrary selection (arbitrary set), and diagonal selection (diagonal set). Experimental results show that the average localization accuracy optimized by the BR-DNN is 3.40 cm with the diagonal set, while the average localization accuracy is 4.35 cm for the arbitrary set and 4.58 cm for the even set. In addition, the training time and positioning time are only 11.25 and 8.66 ms due to a significant reduction of the sparse training data. All of the aforementioned experimental results show that the algorithm and training data optimization we proposed provide a new solution for real-time and high-accuracy positioning with the neural network.
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