# Performance Analysis of Downlink 5G Networks in Realistic Environments
## 5 Fifth-Generation Performance with Multiservice
### Mobility Model  
When mobility is considered, either the transmitter or the receiver is in motion, and then we have a dynamic multipath situation in which there is a continuous change in the electrical length of every propagation path, and thus the relative phase shifts between them change as a function of spatial location. This phenomenon is called fast fading and should be taken into account in the propagation path loss because it causes large and rapid fluctuations in the signal. The rate of change in phase, due to motion, is apparent as a Doppler frequency shift in each propagation path, which is called the Doppler Effect, and accounts for fast fading. Therefore, the amount the frequency changes due to the Doppler Effect depends on the relative motion between the source and the receiver and on the speed of the mobile. Generally, frequency increases when moving toward the base station and decreases when moving away from the base station. The Doppler frequency has been considered to account for the effects of fast fading in the propagation model. Accordingly, Doppler or frequency shifts in the incident plane wave are incorporated, as shown in Figure 5 and referenced in [42].  
Four kinds of users are represented in the simulation, where the amplitude is a Gaussian distribution with mean and standard deviation. The four kinds are as follows [43]:  
(i) Stationary (mean speed \(v_{m}=0\) m/s and mean standard deviation (SD) \(v_{s}=0\) m/s).  
(ii) Slow speed (speed \(v_{m}=4.0\) m/s, SD \(v_{s}=0.5\) m/s).  
(iii) Medium (speed \(v_{m}=8.0\) m/s, SD \(v_{s}=1.0\) m/s).  
(iv) Fast (speed \(v_{m}=12.0\) m/s, SD \(v_{s}=1.0\) m/s).  
Figure 5: Doppler shift increases the signal bandwidth.  
Although the mobility parameters are determined randomly, as soon as the user arrives at the system, they will be assigned one of the above four mobility classes with equal probabilities. Whenever the user moves, his direction is turned by a random angle uniformly distributed between \(-0.25\pi\) and \(+0.25\pi\). Two types of services have been assumed (i.e., voice and data), and the arrival session for both voice and data is modeled as Poisson with the arrival rate, and the service time for the voice calls is exponentially distributed with a call duration of 180 s, whereas the data service time is modeled as a Pareto random variable to represent the self-similar behavior of WWW traffic. Data traffic is assumed to have a mean service time of 100 s. Pareto distribution parameters are given in Table 2.  
For mobility analysis, the following performance metrics are assumed:  
1. Dropping Probability (P\({}_{\rm d}\)) is the probability that the user is excluded from service after the handover.  
2. Blocking Probability (P\({}_{\rm b}\)) is the probability that the new call arrival is rejected based on the CACA conditions.  
3. Grade of Service (GoS = P\({}_{\rm b}\) + 10P\({}_{\rm d}\)) is the combination of the blocking and handover dropping probabilities in a weighted sum. The weighted sum of the new call-blocking probability and handoff-dropping probability is proposed in [43]. This scenario is under the assumption that the dropping probability affects the grade of service with a larger heightening factor than the blocking probability. This is true in real life. Moreover, this weighted average is very useful for most design purposes.