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We propose, numerically analyze and experimentally demonstrate a low-complexity, modulation-order independent, non-data-aided (NDA), feed-forward carrier phase recovery (CPR) algorithm. The proposed algorithm enables synchronous decoding of arbitrary square-quadrature amplitude modulation (QAM) constellations and it is suitable for a realistic hardware implementation based on block-wise parallel processing. The proposed method is based on principal component analysis (PCA) and it outperforms the well-known and widely used blind phase search (BPS) algorithm at low signal-to-noise ratio (SNR) values, showing much lower cycle slip rate (CSR) both numerically and experimentally. For operation at higher SNR values, a hybrid two-stage implementation combining the proposed method and BPS is also proposed and their performance are investigated benchmarking them against the two-stage BPS (2S-BPS). The complexity of the proposed simple and hybrid methods are evaluated against 2S-BPS and computational complexity savings of 92% and 40% are expected for the simple and hybrid methods, respectively.

We present a method for joint optimization of transmitter in-phase, quadrature and inter-polarization time skew, amplitude mismatch, and bias voltages. The method is based on a cooperative coevolutionary genetic algorithm with fitness functions extracted from a directly detected reference QAM signal generated at the transmitter. As a calibration method, it is able to find the values that will statically generate the best possible constellation. To the extent of the simulation investigations conducted, the algorithm is capable to calibrate time skews, bias voltages, IQ phase imbalances, and relative amplitude imbalances with standard deviation of residual error as low as 0.24 ps, 0.019 V, 0.56°, and 0.003, respectively, for a dual polarization IQ modulator with Vπ = 4V and a 16QAM reference signal operating at 16 GBd. An experimental demonstration is also reported.

We present an analysis of clock recovery algorithms in both polarization division multiplexing systems and mode division multiplexing systems. The impact of inter-polarization time skew and polarization mode dispersion in single-mode fibers, as well as the combined impact of mode mixing and mode group delay spread in multi-mode fibers under different coupling regimes are investigated. Results show that although the clock tone vanishing has a known solution for single-mode systems, in multi-mode systems even for low group delay spread, strong coupling will cause clock tone extinction, making it harder to implement an effective clock recovery scheme.

A simple method for joint estimation of transmitter’s in-phase/quadrature and inter-polarization time skew is proposed and experimentally demonstrated. The method is based on clock tone extraction of a photodetected signal and genetic algorithm. The maximum estimation error was 0.5 ps.

A machine learning framework for predicting auto-correlation functions of inter-channel nonlinearities within the uncompensated optical fiber link is proposed. Low generalization error is obtained on the test data.

Linear signal processing algorithms are effective in dealing with linear transmission channel and linear signal detection, while the nonlinear signal processing algorithms, from the machine learning community, are effective in dealing with nonlinear transmission channel and nonlinear signal detection. In this paper, a brief overview of the various machine learning methods and their application in optical communication is presented and discussed. Moreover, supervised machine learning methods, such as neural networks and support vector machine, are experimentally demonstrated for in-band optical signal to noise ratio (OSNR) estimation and modulation format classification, respectively. The proposed methods accurately evaluate optical signals employing up to 64 quadrature amplitude modulation (QAM), at 32 Gbaud, using only directly-detected data.