Chapter 2 Computer Modeling of Transport Layer Effects

This chapter introduces the reader into optical signal representations and the major physical layer effects causing system degradations in the WDM transport layer. Suitable modeling approaches are presented, and typical simulation results are demonstrated. Finally, the chapter focuses on performance degrading effects due to fiber propagation, optical amplification, and signal generation. 2.1 The Value of System-Level Simulation System-level simulations are now routinely used to theoretically evaluate the performance of transmission systems based on the parameters of existing optical equipment, and to define performance requirements for new equipment to ensure robust operation for all required application scenarios. In a sense, system simulation is used as a virtual prototyping tool that reduces cost by shortening the system construction cycle. Furthermore, simulations prove valuable for investigating possible upgrade scenarios, understanding the nature and sources for system-limiting effects, determining the full effect of component limitations, and developing new technological approaches. The most critical part in designing accurate and reliable system simulation scenarios is selection of the right parameters and accurate transport effect models. A. Richter (&) VPIphotonics Division, VPIsystems Berlin, Germany e-mail: Andre.Richter@VPIphotonics.com N. (Neo) Antoniades et al. (eds.), WDM Systems and Networks, Optical Networks, DOI: 10.1007/978-1-4614-1093-5_2, Springer Science+Business Media, LLC 2012 13 2.2 Simulation Domains and Signal Representations For computer simulation to be effective, optical signals need to be represented into the computer. Optical signal representations with different degrees of abstraction are important in providing flexibility for modeling various aspects of photonic networks. Since the initial publications of the fundamental concept as in Lowery et al. [1], many improvements have been introduced supporting the modeling requirements of the ever-evolving advances in optical technologies and network architectures [2]. Time-domain simulations that propagate individual time samples on an iterationby-iteration basis between modeling blocks have been used to investigate, for instance, the properties of integrated photonic circuits, high-speed transmitters, and receivers, where the analysis of bidirectional interactions in the picoseconds range are of key importance [3]. For optical systems and networking applications mixeddomain simulations (i.e. including both, the timeand frequency-domains) are more suitable where arrays of signal samples are passed between modeling blocks. Applications are mainly unidirectional propagation scenarios and bidirectional scenarios where time delays are longer than the array duration (e.g., optical switching scenarios in bidirectional ring networks), or where bidirectional interactions can be averaged in time (e.g., bidirectional Raman pumping of optical fiber). The simulation efficiency of mixed-domain simulations can be controlled by representing optical signals either by their complex, polarization-dependent sampled waveform (time-dynamic or spectral representation), or by time-averaged parameters only. Depending on the modeling scope of interest, the time-dynamic representation can be applied to a single frequency band (SFB), or to multiple (non-overlapping) frequency bands (MFBs) for which the appropriate propagation equations describe the evolution of individual frequency bands, while interacting terms are approximated using frequency decomposition (FD) techniques. Although waveform-dependent propagation effects are ignored when using a parameterized representation of optical signals, this approach provides significant simulation speed-up while keeping sufficient modeling accuracy when investigating, for instance, the impact of ‘far-away’ WDM channels or optical pumps. Parameterized Signals (PS) can also be used to track important properties along the transmission path such as power, principal states of polarization (PSP), as well as accumulated amounts of noise, chromatic dispersion (CD), self-phase modulation (SPM), differential group delay (DGD), and thus easily assess signal characteristics over topology and frequency. Additive noise that is generated by lumped or distributed optical amplifiers can be tracked separately from the optical signal, disregarding nonlinear interaction effects between noise and signal, or accounting for them at the receiver using deterministic system performance estimation algorithms. In that case, optical noise can be described by its wavelength-dependent power spectral density (PSD), which could be step-wise approximated by so-called Noise Bins (NBs) of constant PSD. Further on, the parameterized representation is useful for keeping parasitic terms resulting from optical amplification, crosstalk, 14 A. Richter