# Multiservice-Based Traffic Scheduling for 5G Access Traffic Steering, Switching and Splitting
## 4 Simulation Results and Analysis
### Simulation Results
This indicates that the MSTT scheme is convergent through a limited number of iterations. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**UE Num** & **10** & **20** & **30** & **40** & **50** \\ \hline D utility (\%) & 100 & 100 & 100 & 100 & 100 \\ MDT utility (\%) & 99.658 & 99.765 & 99.639 & 99.772 & 98.683 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Satisfaction of CBR. Figure 5: (**a**) Average throughput of VBR and DS services. (**b**) Average satisfaction of VBR and DS services. As already mentioned in the Introduction section, the size of the DQoS index, \(\theta\), reflects the strictness of the network requirements for QoS. Figure 7 shows the effect of different \(\theta\) values on the normalized effective network capacity. Evidently, the larger the \(\theta\) value, the smaller the normalized effective network capacity. This is because, for larger DQoS indices, more network resources are consumed to guarantee the service QoS index, thereby reducing the normalized effective network capacity [43]. This result is consistent with that obtained theoretically. Additionally, the total effective network capacity increases with increasing transmission power. The network capacity obtained using the proposed MSTT algorithm was evaluated and compared with those obtained using the always-best-connected (ABC) and fixed-ratio power-allocation algorithms. The ABC algorithm is an extended steering mode that is based on the smallest delay. The purpose of the ABC algorithm is to always connect to the optimal link. If it is assumed that the link with the smallest delay is the optimal link, the ABC algorithm is equivalent to smallest delay switching. The power-allocation algorithm essentially obtains the maximum-power SC, with a core concept that includes all the BSs. Thus, the user always connects to the BS receiving the largest RSRP and uses the maximum UE transmit power when uploading data. The main advantage of this ABC power-allocation algorithm is that, although the user can obtain a higher SNR, frequent  
Figure 6: **(a) Normalized RLF for under-loaded, unevenly loaded, and over-loaded scenarios for different \(\omega\); (b) Bandwidth distribution with the number of iterations. **  
Figure 7: Relationship between size of DQoS index \(\theta\) and normalized effective network capacity. network switching increases data transmission delay and greatly deteriorates the user's experience of a delay-sensitive service. In the fixed-proportion power-allocation algorithm, users can allocate certain proportions of power to an NR link and a WLAN link. In our simulation, \(F(x)\) was used to represent \(x\%\) of the total user power allocated to the NR. Figure 8 shows a comparison of the network capacities obtained based on the proposed MSTT algorithm, the ABC algorithm, and the fixed-ratio power-allocation algorithm with varying user-transmit power. The network users are evenly distributed, with \(\theta=10^{-3}\). \(F(100)\), \(F(70)\), and \(F(50)\) represent fixed-ratio power-allocation algorithms, which indicate that users have allocated 100%, 70%, and 50% of the total network power to the NR, respectively. \(F(50)\) also represents the AD algorithm. Evidently, the NWA dual network connectivity is superior to a single network connection. With increasing user-transmit power, the network capacity advantages of dual connectivity became more pronounced given that dual connectivity can effectively offload data on overloaded BSs, and increase the overall effective network capacity via the second link, thereby improving the network capacity. Additionally, the simulation using \(F(100)\) resulted in the worst network capacity because the large bandwidth and high capacity of the WLAN were not fully utilized. The simulation results also indicate that the network capacity achieved using the proposed MSTT algorithm is always better than those obtained using other algorithms, because the MSTT algorithm distributes power as freely and reasonably as possible to achieve the optimal network capacity.