# Overcoming Challenges in Large-Core SI-POF-Based System-Level Modeling and Simulation
## 2 POF Modeling Challenges
In particular, the fiber distance from the source or the active area of the photodetector regulates the angular power distribution that gets into the fiber or the restriction imposed on the received angles, respectively. The relatively high tolerance of POF connections introduces a large uncertainty in the values of these parameters resulting in a high variability in the launching and detection conditions that poses yet another challenge to the predictability of POF transmission parameters. Therefore, comprehensive source and photodetector models must be included in a POF simulation framework, incorporating the necessary parameters to describe their spatial characteristics and their relative positions with respect to the fibers. A theoretical assessment of the variability in input and detected power distributions due to emitter and receiver designs revealed their important contribution to the high standard deviation found in experimental measurements of fiber transmission properties [38]. These factors, combined with other sources of variability such as the changes in fiber properties due to aging or to environmental issues or the presence of passive components, introduce further complexity to the modeling tasks. Ideally, a system-level simulation framework should account for system performance metrics that require a statistical treatment. For instance, the use of air-gap connectors to avoid fiber damage due to vibrations at a physical contact, introduces statistically varying positional shifts during system operation. This uncertainty compounds the already large variability present in experimental measurements of transmission parameters. At a minimum, the ability to configure critical parameter values statistically is needed in a POF-based system-level design. That is, we should be able to assign a distribution, mean, and standard deviation to these values and perform Monte Carlo simulations to estimate system performance. With this in mind, a model to incorporate diameter and numerical aperture mismatches, as well as offsets between two fibers, was used to estimate more realistic coupling losses [39; 40]. Similar approaches were used to estimate the tolerable variability of lateral connector misalignment in the context of an automotive Ethernet over a POF system based on the IEEE 802.3 bv standard and also to assess the impact of statistical connector shifts on the transmission properties in a typical aircraft network with a large number of connectors [41; 42; 43]. Although these works applied statistical analysis to POF systems, they have only included the variability of one or two component parameters. For example, the fiber models are still based on deterministic parameters obtained as the averages of experimental measurements. A realistic model should incorporate confidence intervals for the relevant parameters of the fiber and components to allow for a complete statistical analysis. This is an important goal of our future work and some capabilities to do this are already available in commercial tools [42]. As POFs are being deployed or are under consideration for different application areas, including home networks, networks in cars, and avionics, the operating environment can be harsh and may experience rapid changes, especially in some of the mobile platforms [44; 45]. Therefore, it is imperative that a system designer also takes into account the environmental impacts on system performance. Allowing system designers to estimate the dynamic effects of relevant environmental and operational conditions such as temperature, vibration, stress and dust is also an important feature of a comprehensive simulation framework. This suggests that an understanding of how component performance is affected by the various conditions is prerequisite knowledge, which can be gained through experimentation and subsequently incorporated into the fiber and component models [3; 46; 47; 48; 49; 50; 51].