We compared the performance and the computational complexity of a time-domain- (TD-) artificial neural network (ANN) and a frequency-domain- (FD-) ANN used for nonlinearity compensation in optical fiber communication systems. In optical communication systems, ANNs have been used for coherent optical orthogonal frequency division multiplexing (CO-OFDM) transmission systems in the frequency domain. TD-ANN-based optical nonlinearity compensation has also been investigated in the last few years. For linear equalization to compensate for, e.g., chromatic dispersion (CD), it is known that FD-equalization outperforms TD-equalization in terms of computational complexity over a wide range of CD values. However, TD-ANNs and FD-ANNs have not been investigated in order to compare them in terms of computational complexity, to the best of our knowledge. In this paper, we investigated and compared the computational complexity of a TD-ANN and an FD-ANN which are used for optical nonlinearity compensation. We evaluated the number of complex multiplications needed for nonlinear compensation per symbol. The compensation performance was investigated using 16-ary quadrature amplitude modulation (16QAM) signal transmission over a standard single-mode fiber (SSMF) by numerical simulation. The results showed that the TD-ANN outperformed the FD-ANN in terms of computational complexity.
In coherent optical-fiber communication systems, polarization-division multiplexing is employed to double the transmission capacity. Polarization tracking based on digital signal processing (DSP) is used to cope with the polarization fluctuations of the light wave, which are caused by disturbances of the optical fibers. Usually, the polarization demultiplexing and polarization tracking are performed by using butterfly-structured finite impulse response (FIR) filters. We have proposed and investigated novel methods of polarization tracking using artificial neural networks (ANNs). An ANN can perform polarization demultiplexing because an ANN includes butterfly structures. Adaptive control of the weights of the ANN can be achieved by using decision directed least mean squares (DD-LMS) algorithm. Furthermore, the ANNs can potentially compensate waveform distortion caused by optical nonlinear effects such as self phase modulation (SPM) and cross-phase modulation (XPM). In this paper, we investigated the polarization tracking performance of the ANN under various conditions of polarization fluctuation speed by numerical simulations, comparing with that of FIR filters. Furthermore, we investigated the tracking performance depending on the number of input layer and hidden layer units of the ANN. The results show that the ANN can efficiently track the polarization fluctuation.
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