The phrase "average session throughput" is correct and usable in written English.
It can be used in contexts related to performance metrics, particularly in technology, networking, or data analysis, to describe the average amount of data processed during a session.
Example: "The average session throughput for our application has improved significantly over the last quarter, indicating better performance."
Alternatives: "mean session throughput" or "typical session throughput".
For example, it is observed that the average session throughput is higher in dataset 4 since the cell bandwidth in this network is greater.
In terms of average session throughput, performance of the LTE LC SON slightly surpasses the case where there is no separate macro controllability, although only at higher load levels.
Thus, overall, there will be a smaller percentage of sessions with a low throughput, resulting in a higher average session throughput.
Several key performance indicators (KPIs) can be extracted from the simulator: SINR, statistics on the MCS and transmission rank selection, FD probability, average session throughput (TP), packet delay, etc.
First of all, it is worth highlighting that, in general, the 10th percentile session throughput performance is much more impacted by the choice of the SON load control function, load measure and control parameter used, compared to the average session throughput performance.
The following can be observed: (a) For all load levels, the variable threshold step size adjustment results in improved average session throughput compared to the cases where fixed step size is used for any of the load control policies; (b) The 10th percentile session throughput also improves for the cases of variable step size adaptation in certain load conditions.
A modest performance improvement is also visible in the average session throughputs, in most of the simulated session arrival rates.
The main KPI depicted is average user session throughput, which is the average throughput of a user's session averaged over all sessions in the simulation.
The WLAN LC SON performance is very close to the performance of IRAT LC SON and in certain load conditions it can even improve the average user session throughput by up to 20%%.
Figure 8 shows the average and 10th percentile user session throughput as well the average session percentages per layer of the different LC policies and the baseline.
In our previous work [9], we proposed a way of measuring the quality of an admission control mechanism based on the trade-off between the number of servers used and six important QoS metrics: number of overloaded servers, session throughput, number of aborted sessions, number of deferred sessions, number of rejected sessions and average response time for all admitted sessions.
