Adaptive Compensation of Nonlinear Impairments in Fiber-Optic Systems

Abstract

Optical communication systems are vital for high rate telecommunication. Fiber-optic communication system is an excellent choice due to its low loss, high bandwidth, and robustness to electromagnetic interference. However, fiber-optic links suffer from linear and nonlinear impairments which limit their performance. Digital signal processing techniques can be used for linear impairments compensation. On the other hand, nonlinear impairment is much harder to tackle. There exist two main nonlinear noise which is caused by Kerr effect. Each channel in the fiber-optic link has two poles namely X-pole and Y-pole. In a single channel case, transmitted signal over each pole generates intensity-dependent noise on both poles which is called Self Phase Modulation (SPM) noise. On the other hand, when multiple signal channels co-propagate in a single fiber, the power fluctuations of one signal channel cause a phase shift to another channel, which is due to the Cross Phase Modulation (XPM) effect. Through this thesis, our main contributions are as follows. Firstly, we utilize Low-density parity-check (LDPC) Coded Modulation with Iterative Damping and Decoding at the receiver to overcome the nonlinear noise without any need for feedback to the transmitter. In other words, after extracting the short-term mean of SPM noise, we modify the decoding system to accept a priori information which helps us to remove nonlinearity using demapping. In addition, we propose a joint detection method to compensate for SPM noise. In this method, we exploit two main statistical characteristics of noise samples which are space domain and time domain correlations to improve naive minimum distance detection. In the last chapter, we introduce an algorithm for learning an adaptive model of fiber which can help us not only improve the performance of pre-compensation but also reduce the complexity of the state-of-the-art method.