## 4 Simulation of NDN Transport in V2X Scenarios
### Considerations for a V2X Simulation  
Recent advances in forwarding strategies for connected vehicular environments do only consider very limited/simple models in the simulation of the underlying physical transmission channel(s) (i.e., work presentend in [44] uses Nakagami fading, research in [41] also uses Nakagami fading even in highly dense (with respect to the number of vehicles) highway scenarios). The impact of different propagation models and their effects on the performance of Vehicular Ad-hoc Network (VANET) routing protocols has been shown by [59; 60]. The consensus is that propagation models tremendously impact the performance of those protocols. However, ndnSIM relies on the channel propagation model implementations shipped with ns-3 when it comes down to wired and wireless channels. This restricts the choice of channel models for obtaining path loss to:  
1. Friis' path loss model (free-space, no multi-path propagation, only far field) [61],
2. 2-Ray ground reflection path loss model [62],
3. Log distance path loss model (inside building, densely populated areas) [63],
4. Three log distance path loss model (an enhanced log distance model, for different distances),
5. Nakagami fading [64] (which is not suitable for taking line-of-sight components into account [65]),
6. Fixed receiver signal strength models (matrix propagation model, range propagation model, random propagation model),
7. COST231 propagation model (applicable to urban areas, extends the Hata model which is based on the [PERSON] model) [66],
8. ITU-R 1411 Line of Sight and Non Line of Sight over roof top propagation models [67],
9. [PERSON] propagation model (multi-path fading) [62],
10. [PERSON]'s propagation model [68], and
11. [PERSON]'s propagation model [69]. For the frequency ranges of the listed models we redirect the interested reader to the provided references. Furthermore, most of these models assume that there is at least one stationary entity involved in the communication process and are only valid for scenarios from which they are derived (the prevalent geometry is responsible for the obtained channel characteristics which in many cases has been obtained by a measurement campaign and the model represents a good fit). However, the models listed are only used for obtaining path loss. Currently, there are only two models which allow to obtain the propagation latency/delay implemented in ns-3, the constant speed delay model which uses the distance between transmitter and receiver and the speed of propagation (usually speed of light in vacuum \(c=299\),792,458\(\frac{m}{s}\), slightly reduced in air) and the random delay model. With the upraise of General Purpose GPU (GPGPU) computing and with even more powerful CPUs/GPUs the use of models which are derived from measurement campaigns is questionable (due to their limited applicability). Other very promising channel modeling approaches are ray tracing (where one differentiates between ray launching, deterministic methods, e.g., image method/theory and distributed wave front, or combinations thereof) and geometry-based stochastic channel modeling (GSCM), a stochastic simplified ray tracing approach. The idea of obtaining characteristics of a wireless propagation channel via ray tracing does already exist since the early 90s (i.e., one of the first proposal to use ray tracing is given in [70]). However, it has gained popularity with more powerful GPUs, lately. It has been shown that ray tracing can provide reasonable results in stationary scenarios [71]. GSCM is a mixture between stochastic modeling and ray tracing. Diffuse (mainly small objects, i.e., traffic signs, traffic lights) and discrete (cars, houses; and other significant scattering points) scatterers are placed in the given geometry and line of sight is assumed between these scatterers and the receiving/transmitting entities (scatterers may also be placed according to a given distribution) [65]. These models provide a non-stationary path loss or received signal strength and also delay/latency.