## 4 Numerical Results
### Evaluation of Larger Network Topologies  
To complete the analysis, we evaluate two larger networks: DRING-16 and MESH-20. The number of randomly located RUs is 40 and 30, and the largest cluster of RUs consists of 5 and 4 RUs, respectively, in DRING-16 and MESH-20. As in the RING-10 network, in the analysis we considered different values of the PP capacity multiplier, where \(C\) is between 1 and 3.  
In Figure 7, we show maximum latencies of fronthaul, midhaul, and backhaul flows. Moreover, we report the number of active processing pools in network DRING-16 and in network MESH-20. We can see that the number of active PP nodes decreases if the PP capacity increases. The maximum FH latencies are below 100 us, which indicates that the solutions obtained are correct. In both networks, the maximum latencies of MH and BH flows are below 140 us and 210 us, respectively, which is much lower than the allowable limits. Slightly lower numbers of active PPs in MESH-20 may be explained by higher connectivity of nodes in the MESH-20 network when compared to the DRING-16 network.  
In Figure 8, we present the overall capacity of active PP nodes, expressed in terms of processing units (PUs), in networks DRING-16 and MESH-20. Again, we provide the number of active PPs in the figure. In both networks, we can see that there is some value of \(C\) for which both the number of active PPs is minimized and the overall capacity of the PP nodes is either the lowest (for \(C=2\) in DRING-16) or near to the lowest value (for \(C=2.5\) in MESH-20). This value of \(C\) can be considered as the best one since it minimizes the deployment cost of PPs in the network.  
Figure 6: Maximum latencies of fronthaul, midhaul, and backhaul flows in network RING-10 for scenarios with basic links (**left**) and longer links (**right**).  
Similarly as in the RING-10 network, in Figure 7 we can see that maximum MH flow latencies are equal to 0 if the PP capacity is large enough, namely, for \(C=2.5\) in DRING-16 and for \(C=3\) in MESH-20. As shown in Figure 9a, these cases correspond to the scenarios in which 100% of demands have their DU and CU processing performed in the same PP node and, consequently, the MH flows are not present in the network. In Figure 9a, we can also see that with the smallest required PP capacity (i.e., for \(C=1\)), about 40% of demands have their baseband processing performed in the same PP node. Increasing the PP capacity by 50% (i.e., for \(C=1.5\)), the percentage of joint DU/CU processing increases to 80% of demands. Finally, in Figure 9b, we analyze the average usage of available PP capacity of active PP nodes in different network scenarios. In general, we can observe that the average percentage usage of PP capacity tends to decrease with \(C\). This relationship can be explained by the fact that higher values of \(C\) lead to the increase of the overall processing capacity in the network if the number of active PPs does not decrease significantly (as for \(C>2\) in both networks). Since the DU/CU processing demand in a given network scenario is fixed and the overall processing capacity increases, then the average PP usage must decrease.  
Figure 8: Overall capacity of active PP nodes and number of active PPs for different values of PP capacity multiplier (\(C\)) in networks DRING-16 (**left**) and MESH-20 (**right**).  
Figure 7: Maximum latencies of fronthaul, midhaul, and backhaul flows, and number of active PPs for different values of PP capacity multiplier (\(C\)) in networks DRING-16 (**left**) and MESH-20 (**right**).