# Performance Model for Video Service in 5G Networks
## 3 Approaches
### Network Design Approach  
After selecting the interference coordination approach, we can construct the performance model to map the SLA requirements to the network attributes. For video service, SLA requirements include the traffic demand (or the number of UE that fulfills specific service QoS requirements), and the network attributes include the allocated bandwidth among the services and the ISD. Other attributes, i.e., the average UE distance from the site, and variance in traffic demands among sectors can be used to fine-tune the performance model. Algorithm 3 is used to create a performance model. The algorithm maps the input variables (_s_, \(u\), \(b\), _isd_, _dis_, and _varT_) to the required bandwidth. UETHRESHOLD is defined as percentage of UEs satisfied the QoS requirements, here we 95%. Multiple drops with different random seeds are used to generate data sets used for data analytics and machine learning. For the network design, the worst case is considered. Thus, for each input variable data point (s, \(u\), \(b\), _isd_, _dis_, and _varT_), the maximum required bandwidth is calculated, and the performance model is represented by maxBW (_s_, \(u\), \(b\), _isd_, _dis_, and _varT_) for each service. The goal is \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**UE** & **ServingCell** & **ConflictUE** & **ConflictBM** & **ConflictCell** & **ConflictUEUtil** & **ConflictUEOfUtil** & **ConflictUEOnUtil** \\ \hline \(UE_{1}\) & \(RRU_{11}\) & \(UE_{x1}\) & \(Beam_{x1}\) & \(RRU_{x1}\) & \(Utility_{x1}\) & \(Utility_{x1}\) & \(Utility_{x1}\) \\  & \(UE_{x2}\) & \(Beam_{x2}\) & \(RRU_{x2}\) & \(Utility_{x2}\) & \(Utility_{x2}\) & \(Utility_{x2}\) & \(Utility_{x2}\) \\ \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\ \(UE_{x}\) & \(RRU_{xx}\) & \(UE_{xy}\) & \(Beam_{xy}\) & \(RRU_{xy}\) & \(Utility_{xy}\) & \(Utility_{x}\), \(vOf\) & \(Utility_{x}\), \(vopen\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Forbidden Relationship [23]. Figure 4: Interference Effect. to approximate the mapping function so that when there are new input variables data points, the required bandwidth can be predicted. See Figure 3 for the approach. We observed that when more drops are used for learning, a lower number of the SLA violations happen. ```
For service \(s\), number of UE \(u\), number of beams \(b\), and ISD \(isd\), the average UE distance to serving cell \(dis\), and traffic demand variance \(varT\) Initial with small Bandwidth: \(W_{0}\) loop: With picked IC, calculate QoS (s, u, b, isd, \(W_{0}\), \(dis\), \(varT\)): For all cell \(j\) from cluster \(i\) in network \(n\) For all UE \(k\) in cell \(j\) If QoS (\(k\)) > QoSRequirement Valid(k) = true; End if End For while TotalValidUE < UETHRESHOLD \(W_{0}\) * = 2; deltaW = \(W_{0}\); Go to loop Else \(W_{0}=W_{0}-\text{deltaW}\); Break; Iteration = N; For I = 1:N If TotalValidUE < UETHRESHOLD deltaW = deltaW/2; Else deltaW = \(-\text{deltaW}/2\) End If With picked IC, calculate QoS (s, u, b, isd, \(W_{0}+\text{deltaW}\), \(dis\), \(varT\)) End For End For
```  
**Algorithm 3** Performance model construction  
Given a total bandwidth of \(W\) and demand \(d\), considering a finite number of services and using interpolation, an exhaustive search can be performed to find the best fit \(w\) and corresponding ISD. Algorithm 4 shows the approach. ```
For total bandwidth \(W\) Initial with ISD: \(isd_{0}\) loop: Calculate users per site for each service s Calculate the bandwidth per service \(w_{s}\) using the performance model Sum up bandwidth \(\text{w}=\frac{5}{1}\,w_{s}\) If w < W save \(isd_{0}\) and \(w_{s}\); \(isd_{0}=isd_{0}+\text{delta}\); go to loop Else return saved \(isd_{0}\) and \(w_{s}\) of previous iteration End If End For

### Network Optimization Approach

The design process is calculated for the worst-case scenario. As video traffic has a long duration, a dynamic network optimization approach is proposed to adjust the bandwidth to improve resource usage.