previous section, the traffic increase causes inter-cell interference increase which is visible as CQI decrease. The CQI decrease in turn means that the spectral efficiency decreases and therefore the end-user throughput decreases. On the other hand, the increase in the number of connected users which are downloading at the same time causes additional throughput per user degradation as the cell radio resources are shared among all the users. The throughput per user can be estimated based on the cell-level counters, for example, simply dividing the cell-level throughput by number of users which are having data in the eNodeB buffers (i.e. those users which are currently downloading something). The cell-level Capacity Optimization throughput in turn can be estimated by dividing the delivered user plane bits by the number of active TTIs used to transmit those bits. The number of bits should contain only the user plane data bits excluding the retransmissions, HARQ or RLC retransmissions. The active TTI is a bit difficult to measure as the active TTI is active regardless of the number of PRBs used during the TTI. Therefore, this type of performance indicator is not very accurate in case the PRB utilization (how many PRBs are used from all available) is very low (<30%). Scaling the cell throughput for, for example, 10% PRB utilization to 100% PRB utilization simply by dividing the cell throughput by the PRB utilization cannot be done. This is due to the fact that such a simple scaling assumes that the downlink interference conditions do not change at all when the PRB utilization increases and such assumption is not correct. Therefore, in case the cell throughput performance indicator is calculated using the simple formula described above the results should be analysed carefully taking into account the PRB utilization. Figure 13.36 shows the example of average throughput per user (calculated as described above) as a function of the number of RRC-connected users. The cells included into the analysis are high traffic cells