Abstract
In recent years, it has been established that the adverse effects of nonlinear interference noise (NLIN) can be mitigated using adaptive equalization methods. As such, a wide variety of adaptive equalization methods have been used to treat nonlinearity, in different transmission scenarios. This paper reviews the principles of out-of-band nonlinearity mitigation using adaptive equalization. Statistical properties of NLIN that can be exploited for mitigation are discussed, as well as the cost and benefit of various types of equalizers. In particular we describe the equivalence between the NLIN and time-dependent inter-symbol-interference (ISI) and discuss ways in which the ISI coefficients can be characterized theoretically and experimentally. We further discuss the effectiveness of existing ISI mitigation algorithms, and explain the need for designing customized algorithms that take advantage of the various correlation properties characterizing the ISI coefficients. This paper is intended to be a practical reference for researchers who want to apply equalization algorithms or design new methods for nonlinearity mitigation.
Highlights
The challenge of mitigating nonlinear interference in wavelength division multiplexed (WDM)networks has attracted significant efforts
The sources of nonlinearity can be divided into two groups, as illustrated in Figure 1: in-band nonlinearity, which involves interactions of frequency components that are within the receiver’s bandwidth, and out-of-band nonlinearity, which is produced by interaction with WDM channels that are outside the received bandwidth
The treatment of these two nonlinearity sources is very different; in-band nonlinearity can be viewed as a deterministic signal-dependent effect, and it can be effectively reduced by means of digital back propagation [1], sequence detection [2], or Volterra-series equalization [3]
Summary
The challenge of mitigating nonlinear interference in wavelength division multiplexed (WDM). The sources of nonlinearity can be divided into two groups, as illustrated in Figure 1: in-band nonlinearity, which involves interactions of frequency components that are within the receiver’s bandwidth, and out-of-band nonlinearity, which is produced by interaction with WDM channels that are outside the received bandwidth The treatment of these two nonlinearity sources is very different; in-band nonlinearity can be viewed as a deterministic signal-dependent effect, and it can be effectively reduced by means of digital back propagation [1], sequence detection [2], or Volterra-series equalization [3]. If the symbol rate is significantly lower and the number of channels is higher, these effects may becomes the dominant noise source [4] As such systems are not common, we do not consider this regime in the paper).
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