e to the level of required aggregation level, the UE cannot be sched- uled in corresponding TTI and the data remains in the eNodeB buffer (and scheduling delay increases). Both high PRB allocation and CCE blocking cause increase in scheduling delay LTE Small Cell Optimization Average DL scheduling delay (ms) Figure 13.52 Uplink and downlink physical resource block utilization versus average downlink packet scheduling delay per cell and per 15 minutes and increase in number of users with data in buffer in uplink and downlink and therefore lower end-user-experienced throughput as shown in Figures 13.54 and 13.55, respectively. As shown in Sections 13.3 and 13.4, uplink and downlink quality can also limit the net- work performance and capacity. Each new user that has some data to be transmitted causes small reduction in uplink SINR and downlink CQI as shown in Figures 13.56 and 13.57, respectively. The example cells are clearly uplink limited due to the PUSCH SINR collapse when the number of users with data in buffer in uplink increases (as indicated in Figure 13.56) and Average DL scheduling delay (ms) Figure 13.53 Control channel element allocation blocking versus average downlink scheduling delay per cell using 15-minute statistics Capacity Optimization 10,000 Average number of users with data in buffer UL Figure 13.54 Uplink throughput per user as a function of number of users with data in uplink buffer per cell using 15-minute statistics no impact to average CQI value when number of users with data in buffer in downlink increases. When the number of users with data in buffer increases also the end-user-experienced uplink and downlink throughputs rapidly decrease as shown in Figures 13.54 and 13.55, respectively. The number of users with data in buffer (uplink and downlink) increases as a function of number of RRC-connected users as shown in Figure 13.58 for one mass event. The capacity limits for each cell can be calculated based on the number of RRC-connected users required to be supported and predictions can 