Digital signal processing for fiber optic communication systems

Abstract

The global internet protocol (IP) traffic is rising exponentially due to the increased number of bandwidth-intensive services like video-on-demand and cloud computing. To meet the demands of growing traffic, the data rates of fiber optic communication systems (FOCSs) need to be increased. In this regard, digital signal processing (DSP), which already plays a powerful role in the modern FOCSs, is being explored. Increasing the net data rate of FOCSs requires compensation for nonlinear impairments that can arise from the Kerr effect during the propagation of signal through fiber as well as from the non-ideal responses of transceiver hardware components. Furthermore, cutbacks in net data rates due to training overheads, i.e., the non-information carrying part of transmitted data consumed by DSP algorithms; need to be reduced. In this dissertation, we propose novel DSP approaches to address these problems of great practical interest.

The Kerr nonlinear effects add phase shifts to the signal, which are dependent on its instantaneous power. These phase distortions occur simultaneously with the dispersion effect of the fiber, which spreads signal pulses in time. The interplay is complicated and makes compensation of distortions challenging. The nonlinear Fourier transform(NFT), which offers immunity from the distortions of the Kerr effect, received great interest in recent years. The lossless nonlinear Schrödinger equation (NLSE), which models signal propagation in an ideal lossless optical fiber, belongs to a class of nonlinear partial differential equations known as integrable equations. These integrable equations can be solved exactly by NFT. Similar to the Fourier transform that translates a linear dispersive propagation in the time domain into phase delays in the signal spectrum, the NFT translates the nonlinear evolution of the signal governed by the lossless NLSE into trivial multiplications in the nonlinear Fourier spectrum of the signal. The NFT is exact for lossless fiber channels. In the presence of loss, the integrability property is violated. In lossy propagation, signal power reduces as it propagates. This in turn reduces the strength of the nonlinear effects along the length of the fiber. As practical fibers are lossy, the path-average approximation is often used to apply NFT on lossy fiber channels. In this approximation, the variation in the Kerr nonlinear effects due to the reduction in signal power is accounted as the variations in the Kerr-nonlinearity parameter of the fiber. Then, by approximating the varying Kerr-nonlinearity parameter with its average value over a span, a lossless fiber model is obtained. This approximation has errors associated with it which sacrifices the performance. We developed a NFT-based transmission system that is exact even in the presence of fiber loss. The proposed design eliminates errors due to loss, thus improving performance over the design that uses path-average approximation…