# Performance Model for Video Service in 5G Networks
## 3 Approaches
### Network Design Approach
For simplicity, we fixed the cell radius to 100 m, the average number of users per sector at 10, the beams to cover the sector area at 8, and the service as S2. The first 1000 drops were used to generate the initial performance model. For each new drop, \(dis\) and \(varT\) were used to search the performance model for a solution and verified whether the solution fulfilled the QoS requirements. If not, a new solution had to be found using Algorithm 4 in Section 3, and the performance model updated. The figure shows that as more drops were run, the rate of successful prediction increased. Figure 10: Design performance model of the Virtual Reality service. The sub-figure (**a**) shows required bandwidth vs. number of UEs per sector at different ISD, the sub-figure (**b**) shows required bandwidth vs. cell radius at different number of UEs per sector. Figure 9: Bandwidth for multiple drops. The sub-figure (**a**) shows that as the average distance from the UE to the BTS increases, more bandwidth is required. The sub-figure (**b**) shows that as the traffic demand varies among the sectors, the variance of the required bandwidth to fulfill the QoS requirement increases

Figure 11: Design performance model of the Interactive Gaming service. The sub-figure (**a**) shows required bandwidth vs. number of UEs per sector at different ISD, the sub-figure (**b**) shows required bandwidth vs. cell radius at different number of UEs per sector. Figure 12: Design performance model of the Mixed Traffic service. The sub-figure (**a**) shows required bandwidth vs. number of UEs per sector at different ISD, the sub-figure (**b**) shows required bandwidth vs. cell radius at different number of UEs per sector. Figure 13: Design solution. The same setup in Figure 14 is in Figure 15, to show the network optimization results for 1000 random drops. On average, a bandwidth saving of 7.5% was achieved, which shows that the bandwidth we used for the initial design is as close to the optimized bandwidth as we can achieve. ## 6 Complexity Analysis

For the IC approach, additional channel quality indicator (CQI) feedbacks are required for cluster edge UEs. For FWA applications with slow varying channels, the air-link overhead is small. The 5G network uses a dedicated fiber network to connect the baseband unit (BBU) and the remote radio unit (RRU), or between the BTS to exchange intra-cluster scheduling outputs and forbidden relationship tables, both delay and data volume are not of concern. Figure 5:0 SLA violation rate. Figure 5:1 Network optimization. For computational complexity, multiple SINR values are calculated. On the BBU, the intra-cluster scheduling uses greedy algorithm with complexity of \(O\big{(}|C|*~{}U_{c}^{2}\big{)}\), where \(|C|\) is the number of clusters and \(U_{c}\) is the number of UE in each cluster, which equals \(C_{max}\times U_{s}\). \(C_{max}\) is the maximum cluster size, and \(U_{s}\) is UEs per sector. For inter-cluster scheduling, only scheduled cluster-edge UE triggers inter-cluster scheduling, with complexity of \(O(N*\log(N)*~{}U_{e})\), where \(U_{e}\) is the number of assigned cluster-edge UE, \(N\) is the number of cell, and \(O(N*\log(N))\) is the sorting complexity. For the network design and optimization approach, fewer messages are transferred between the network and the management unit, and they are not in a time-critical situation, delay and data volume are not of concern. For computational complexity, the search algorithm is used for the network design. The algorithm runs offline and with low complexity. ## 7 Conclusions

In this paper, we introduced a two-level IC approach to improve performance models that map high-level customer-friendly business requirements to low-level network parameters and achieve QoS assurance for delay-sensitive traffic.