We present a general overview of the role of computer models in the design and optimization of commercial optical transmission systems. Specifically, we discuss (1) the role of modeling in a commercial setting, (2) achieving the proper balance between accuracy and computation speed, (3) model verification against experiment, and (4) case studies demonstrating the benefits of modeling. Ideally, experiments are preferable to models when describing system performance, particularly to support claims of a system's functionality to a customer. However, modeling is often the only choice for many of the problems that a commercial networking company must solve. Because there are design parameter spaces that are either too expensive or too time-consuming to verify experimentally, the main role of modeling in industry is to study what experiments cannot. For example, when an analytical solution of a statistical problem is infeasible, a common modeling solution is to perform Monte Carlo trials to study the statistical behavior. Another typical modeling task involves looking at variations of hardware that would be prohibitively expensive to acquire and test. Implementing modeling in industry involves a balance between three needs: cost-efficiency, time-efficiency, and accuracy. We will discuss the approaches we have taken at PhotonEx to meet these needs: leveraging academic research, developing "reduced models" and utilizing computational clusters. Specifically, we will use case studies to illustrate the application of these approaches to modeling long-haul optical transmission systems.

We show that electrical domain (post-detection) approaches hold great promise for mitigating distortions such as polarization mode dispersion (PMD) in optical communication systems, when they are used by taking the physical characteristics of the distortion into account. We present a polarization diversity receiver for PMD mitigation and show its effectiveness by simulation results using accurate modeling of the PMD distortion. We also note that by incorporating electronic equalization into the diversity receiver structure further performance improvements can be achieved. We evaluate the system outage probability by calculating the power penalty due to PMD distortion and show its agreement with the simulation results.

We introduce a signal processing approach to compensate for the timing jitter produced by the acoustic effect in soliton communications. The other main sources of timing jitter, the Gordon-Haus effect and the polarization effect, are inherently stochastic. By contrast, the acoustic effect is deterministic and becomes the dominant source of bit error rates in standard soliton systems when the bit rates are more than 10 Gbits/s and the transmission distance is more than several thousand kilometers. We exploit the deterministic nature of the acoustic effect to introduce a scheme that predicts the amount of timing jitter as a function of the previous transmitted bits and uses the information to adjust the sampling period of the received soliton pulses. We demonstrate successful application of the scheme by simulations and discuss implementation issues.