## 4 Performance Evolution
As we can see, Optimized-RR outperforms the other two algorithms in terms of the upstream delay performance. We can see from this figure that Optimized-RR DBA shows a superior delay performance as compared to the gGIANT. This is due to the efficient utilization of XGS-PON upstream bandwidth by Optimized-RR DBA, because of allowing ONUs to adjust their transmission window to accommodate the remainder of the unallocated portion of the upstream frames during every allocation cycle. From the same figure, we notice that Optimized-RR DBA attained a delay performance in the range of 290 to 300 \(\upmu\)s when per-ONU/RRU traffic load ranged from 903 to 922 Mbps (i.e., the required front-haul uplink capacity when considering PHY split-based small cell [26]), while gGIANT shows a delay performance higher than 300 \(\upmu\)s for the same range of traffic loads. When it comes to the Adaptive-Learning DBA, we can see that it attains a delay performance in the range of 200 to 205 \(\upmu\)s for the ONU/RRU traffic load ranging from 903 to 922 Mbps, which is not only much lower than the other two algorithms but also satisfies the latency requirement for mobile front-haul recommended by the next-generation mobile networks alliance (NGMN) i.e., 250 \(\upmu\)s. The low-latency performance achieved by Adaptive-Learning DBA is because of eliminating one-way propagation delay for receiving the buffer occupancy report from ONU as well as the waiting time for DBA processing at OLT, as illustrated in Figure 8a,b. We can see from Figure 8a that for Adaptive-Learning DBA the end-to-end delay budget includes (Queuing delay at ONU (Tq) + one-way propagation delay for sending the grant from OLT to ONU(Tg) + one-way propagation delay for transmitting the data from ONU to OLT (Tf)). However, for gGIANT and Optimized-RR DBA, as we can see from Figure 8b, on top of the delay budget calculated in the case of Adaptive-Learning DBA, there is a one-way propagation delay for sending the reports from ONU to OLT (Tr) in addition to the DBA waiting time (Td) that must be included in the calculation. Figure 7b shows the utilization performance comparison between the three algorithms. From this figure, we can see that the highest utilization performance is achieved by Adaptive-Learning DBA. This is for two reasons: one, the faster grant assignment process that can be achieved by Adaptive-Learning DBA, which allows ONUs to effectively utilize XGS-PON upstream bandwidth compared to the two other DBAs; two, the Adaptive-Learning DBA also inherits the property of dynamic adjusting of the ONU pre-determined limit, the same as Optimized-RR DBA. This allows ONUs to efficiently exploit any unallocated remainder of the upstream frames, improving the overall XGS-PON upstream link utilization. Figure 7c shows the packet loss ratio performance comparison of the three algorithms. As we can see, Optimized-RR DBA achieves a lower packet loss ratio compared to gGIANT. This is due to the reduction in the network congestion because of allowing ONUs to use a wider transmission window to transmit their data, and this minimizes the probability of dropping the packets from the ONU buffer. From the same figure it can be clearly seen that our new adaptive-learning-based DBA  
performs better than Optimized-RR and provides a lower packet loss rate. The reason for this is that the adaptive-learning-based DBA increases the speed of serving ONU. In other words, it clears the ONU buffer data much faster than optimized-RR, providing a significant reduction in network congestion and waiting time for the packets to get served. Figure 7d shows the jitter performance comparison of the three algorithms. From this figure, we can see that the Adaptive-Learning DBA achieves the lowest jitter performance in comparison with the other two DBAs. This is due to a significant reduction in the network congestion, as the variation of delay between two consecutive packets is highly affected by the network congestion.