# C-RAN Traffic Aggregation on Latency-Controlled Ethernet Links
## 4 Numerical Results  
To evaluate the benefits introduced by the proposed mechanism, an event-driven simulator in C++ language has been developed. One RRH generates a CPRI flow according to two different options with rates \(R_{CPRI}=614.4\) Mbps (option 1) and 6.144 Gbps (option 6). The IHON fixed delay \(\Delta=99.2\) ns is assumed, which corresponds to the smallest segment (124 Byte) that can be preempted [29]. A time guard of 10 ns between frames is applied. A single output channel (\(m=1\)) with rate \(R_{W}=10\) Gbps is considered. The number of CPRI basic frames in a guaranteed burst \(N_{F}\) is varied over the intervals \([1,70]\) and \([1,7]\), for CPRI option 1 and 6, respectively [27], so that the payload length \(L_{F}\) varies accordingly. A set of simulations varying the average BH packet length \(L_{B}\) is obtained with a load \(\rho_{B}\) such that a BH packet is always ready for transmission on the output channel. The length of BH frames is considered to be exponentially distributed with parameter \(L_{B}\). Figure 5 shows the success probability of the BH traffic, defined as the ratio between the packets not interrupted and the total packets in service, as a function of \(L_{F}\), for both CPRI options, varying \(L_{B}\). In both cases, the success probability increases with \(L_{F}\), due to the resulting larger \(T_{gap}\). Option 6 shows lower performance than option 1 due to the smaller size of the gap, especially when \(L_{F}\) is low, thus suggesting to use larger \(N_{F}\) in this case. However, increasing \(N_{F}\) increases the encapsulation delay, which may impact the maximum reach of the FH connection. Figure 4: \(T_{GAP}\) as a function of different values of payload length \(L_{F}\) for CPRI opt. 1 and 6 on a 10 Gbps line. Figure 6 reports the BH throughput, normalized to the output line rate (10 Gbps), as a function of \(L_{F}\) for option 1 varying \(L_{B}\). The figure also reports the maximum normalized capacity left by FH traffic. The value of throughput in the case of the P policy reaches 8.9 Gbps only for high values of \(L_{F}\) with quite a limited influence of \(L_{B}\). The S policy, instead, is able to better exploit the available capacity for any value of \(L_{F}\), except for the influence of the transmission guard times inserted. The same evaluation obtained for option 6 in Figure 7 shows a remarkable effect of the shorter gaps in the FH flow, which also prevents the F policy to fully exploit the available capacity for low values of \(L_{F}\), due to the high numbers of segments needed and related inserted transmission guard time. Figure 5: Backhaul (BH) success probability as a function of payload length \(L_{F}\) for different BH packet length \(L_{B}\) using CPRI opt. 1 and 6 without segmentation. Figure 6: Backhaul (BH) throughput, normalized to the output link capacity, as a function of payload length \(L_{F}\) for different BH packet length \(L_{B}\) using CPRI opt. 1. Solid lines for the case with segmentation (S), dashed lines for the no-segmentation case (P). Maximum normalized capacity by fronthaul (FH) is shown by a solid line. Figure 8 reports the overhead introduced by the P and S policies calculated as the ratio of the number of bytes for Ethernet headers and the the total number of bytes transmitted as BH traffic for option 1. The same evaluation is presented in Figure 9 for option 6. The effect of the S policy is more evident with option 6 where, due to the smaller gaps in the FH flow, multiple segments are typically required to transmit each BH packet. In any case, the additional overhead is quite limited when increasing \(L_{F}\). It is interesting to analyze the average number of segments to transmit a BH packet in option 1 and option 6, as shown in Figure 10 for the S policy. Option 1 allows transmission of a packet as a single segment in most cases for any \(L_{F}\).