UC San Diego

Ultra-high capacity fiber optic systems with data rates exceeding 100 Giga bits per second per fiber are currently being deployed with higher capacity systems in development. The requirement of a minimum energy per bit for reliable communication means that the power launched into a single fiber is now at a level where significant nonlinearites exist. Nonlinearities can also be produced for lower power intensity-modulated systems because of the square-law nature of sensors.

In order to maximize the information capacity, the combined channel that includes a combination of nonlinear impairments along with additional linear impairments must be mitigated. This mitigation can be achieved by a combination of modulation coding at the transmitter and equalization at the receiver. The development of these techniques for nonlinear channels is significantly more complex than the corresponding techniques for linear channels because of the nature of the nonlinearity and the extremely high data rate. This rate limits the complexity of the equalization algorithm.

This thesis presents modulation coding and equalization techniques for several nonlinear fiber optic channels. We consider two classes of nonlinearity. The first arises from the combination of linear dispersion in an optical fiber and square-law sensing. The second arises from nonlinear propagation characteristics caused by a power-dependent index of refraction change called a Kerr nonlinearity.

A variety of nonlinear channel models can be constructed from these two fundamental forms of nonlinearity along with linear impairments. The dominant linear impairment is dispersion. One form occurs when the propagation characteristics for each mode depend on the frequency. A second form of dispersion arises because different polarization modes can have different propagation characteristics.

The research premise of this thesis is that a combination of modulation coding, sequence estimation (both single-user and multi-user) and nonlinear equalization based on heuristic algorithms can produce significant performance improvement relative to published techniques. We present several abstracted scenarios reflecting practical systems where a combination of these techniques is effective. We also describe situations where they are ineffective. These results lay the foundation for further work using these techniques to optimize specific nonlinear channels.