KEYWORDS: Modulation, Digital signal processing, Optical communications, Telecommunications, Tolerancing, Signal to noise ratio, Fiber lasers, Chromium, Optical networks, Optical amplifiers
A modulation format identification (MFI) method based on the nonlinear power transformation via logistics regression is adopted for coherent optical receives systems. The amplitude variance, fourth power transformation and fast Fourier transform of input signals are utilized for special features extraction in our work. Five typical optical modulation formats (i.e.,16/32/64QAM and Q/8PSK) with the transmission rate of 28 GBaud are numerically simulated to demonstrate the feasibility. The simulation results show that our method has great performance even under low optical signal noise ratio (OSNR). Compared with the MFI algorithm based on Stokes space and asynchronous delay tapped sampling, our MFI algorithm requires less time to achieve similar performance of optical receive systems. Especially, this method exhibits tolerances to the laser linewidth and nonlinearity.
Curve fitting algorithm is traditionally utilized for Brillouin optical time domain analyzer (BOTDA) temperature extraction, with high time cost. To improve the extraction speed, general regression neural network (GRNN) is introduced into BOTDA temperature extraction. As a feed-forward network, only one parameter is needed to be learned in GRNN and its structure is self-adaptive for various samples with different scale, making it easier to train. The performance of GRNN is investigated in simulation and experiment different signal-to-noise ratios, pump pulse widths, and frequency scanning steps. The results show that, with the similar or better accuracy, GRNN achieves faster processing speed which is 7 times over curve fitting methods. The fast processing speed, and high extraction accuracy make GRNN approach a potential way of real-time BOTDA temperature extraction.
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