Optical communication systems represent the backbone of modern communication networks. Since their deployment, different fiber technologies have been used to deal with optical fiber impairments such as dispersion-shifted fibers and dispersion-compensation fibers. In recent years, thanks to the introduction of coherent detection based systems, fiber impairments can be mitigated using digital signal processing (DSP) algorithms. Coherent systems are used in the current 100 Gbps wavelength-division multiplexing (WDM) standard technology. They allow the increase of spectral efficiency by using multilevel modulation formats, and are combined with DSP techniques to combat linear fiber distortions. In addition to linear impairments, the next generation 400 Gbps and 1 Tbps WDM systems are also more affected by the fiber nonlinearity due to the Kerr effect. At high input powers, fiber nonlinear effects become more important and their compensation is required to improve the transmission performance.Several approaches have been proposed to deal with the fiber nonlinearity. In this paper, after a brief description of the Kerr-induced nonlinear effects, a survey on fiber nonlinearity compensation (NLC) techniques is provided. We focus on the well-known NLC techniques and discuss their performance, as well as their implementation and complexity. An extension of the inter-subcarrier nonlinear interference canceler approach is also proposed. A performance evaluation of the well-known NLC techniques and the proposed approach is provided in the context of Nyquist and super-Nyquist superchannel systems.

The defect and impurity states in ZnO nanocrystals synthesized using the plasma arc technique can be modified to optimize the nonlinear optical properties for optoelectronic and biophotonic applications. Highly efficient second harmonic signals over a wide range of near-infrared wavelengths, spanning from 735 nm-980 nm, has been observed and can be used in biological imaging. The use of further high energy excitation ranging from 700 nm-755 nm leads to two-photon absorption and yields broadband two photon emission extending from the 370 nm-450 nm wavelength regime which can be useful for therapeutic applications.

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This paper proposes a signal-to-noise ratio (SNR) estimator based on recurrent neural network (RNN) in optical fiber communication links. The proposed estimator jointly estimates the linear and nonlinear components of the SNR. The input features of the proposed estimator are carefully designed based on a combination of the lower quartile and entropy extracted from the received signal. The proposed input features do not require knowledge of the transmitted symbols. In the proposed SNR estimator, three different RNN models are investigated, namely simple RNN, gated recurrent units, and long shortterm memory. The overall computational complexity of the three models of the proposed estimator, including the feature extraction and RNN structures, are analyzed. Numerical results show that the three models of the proposed estimator provide a trade-off between the complexity of the RNN structure and estimation accuracy. Furthermore, the proposed estimator achieves a better SNR estimation accuracy and reduces the overall computational complexity compared to the literature.