## 4 Dynamic BWP Allocation: Dynamic Multiplexing vs. Orthogonal Slicing
### Simulation Results  
In this section, the throughput of eMBB was calculated using Monte Carlo simulations in a downlink system where eMBB and URLLC AVs coexist to verify the proportional relationship given in (10). We assumed ten eMBB AVs and one URLLC AV, which share a CBW of 100 RBs. In dynamic multiplexing, impact of URLLC to eMBB is mainly governed by how much eMBB resources are punctured by URLLC traffic. The amount of eMBB resources that are punctured does not depend on OFDM numerology of URLLC. It is attributed to the fact that the amount of time-domain resources reduced by the larger SCS is equivalent to that of frequency-domain resources increased [17]. As we are assuming an ideal interleaving for eMBB, its performance is not variant as long as the same amount of resources are maintained. It implies that our performance would be the same for the different numerologies with the different SCS, i.e., requiring the same D threshold. Without loss of generality, therefore, we just assume the same OFDM numerologies with \(\mu=0\) for both eMBB and URLLC traffic in the current analysis. To focus only on the throughput performance, control overheads such as reference signal, preemption indication, and HARQ retransmissions were not considered explicitly in the current analysis. In particular, however, we can still assume that retransmission load is a part of a traffic load considered in the simulation [18; 19; 20]. The URLLC traffic would be mostly originated by time-critical applications, e.g., flight control messages, with random arrivals [21]. More specifically in our simulation, we assume a full buffer model for eMBB traffic with a fixed high data rate while employing a Poisson arrival model for URLLC traffic. Furthermore, we adopt lower levels of MCS for URLLC service, which usually requires a robust performance. Table 2 presents the MCS index table for the physical downlink shared channel (PSDCH) of both eMBB and URLLC AVs as prescribed in the 3 GPP specification [22]. In our simulation, the MCS index of URLLC AV, which is denoted as \(I_{\text{MCS}}^{URLLC}\), is set to have one of the low MCS levels with QPSK modulation, i.e., \(I_{\text{MCS}}^{URLLC}=0,1,2,\ldots,9\), for the most robust performance. Figure 4 shows the capacity regions of DM and OS. It indicates the throughput of eMBB AVs obtained by changing the traffic load \(D\). The capacity regions were observed by varying the URLLC MCS index and assigning a random MCS index for eMBB, which is uniformly distributed over all possible MCS indices from 0 to 28. The code rate of MDS code for the given MCS index of eMBB also follows Table 2. Meanwhile, we considered \(I_{\text{MCS}}^{URLLC}=0\), 4, and 8. It is obvious that the throughput of eMBB improves as \(I_{\text{MCS}}^{URLLC}\) increases, thereby requiring less resources for URLLC AV. As shown in Figure 4, a higher MCS index increases the capacity region, where DM outperforms OS. Furthermore, DM outperforms OS up to a certain level of URLLC load, which implies that OS becomes more effective when a system is overloaded with URLLC traffic. In other words, there exists a threshold level of URLLC load, denoted as \(D_{\text{threshold}}\), which can be a measure of durability of eMBB traffic against puncturing by URLLC traffic. Because the URLLC traffic requires \begin{table}
\begin{tabular}{c c c c} \hline
**MCS Index \(I_{\text{MCS}}\)** & **Modulation Scheme** & **Code Rate \(\times\)[1024]** & **Spectral Efficiency** \\ \hline  
\hline \end{tabular}
\end{table} 
Table 2: MCS index table for Physical Downlink Shared Channel [22]. less resources to achieve a higher MCS level, \(D_{threshold}\) increases with higher MCS indices of URLLC AV. Figure 5 shows the capacity regions for DM and OS while varying the MCS level of eMBB AVs. More specifically, we set \(I_{MCS}^{eMBB}=10\) and \(I_{MCS}^{eMBB}=16\) while fixing \(I_{MCS}^{URLLC}=0\).