h users in the cell, and in turn the achieved cell throughput is decreasing as well, as shown in Figure A.14. While observing other traffic models, it shows that as long as the cell capacity is not fully utilized (the average noise rise is below the maximum allowed noise rise of 6 dB), the obtained overall average application throughput get increased if the traffic model generates more uplink traffic. Overall Average App. Throughput 1M 15s 3GPP FTP 100K 15s 50K 15s 10K 15s Number of UEs Figure A.16: Overall average application throughput Figure A.17 shows the Coefficient of Variation (COV) of the cell throughput. It implies the variation and burstiness of the traffic. It is seen in Figure A.17 that in general the COV of cell throughput declines when the number of users increases, regardless which traffic model used. And the lower the traffic demand produced by the traffic model, the higher the COV values. It can be seen that the ftp traffic model V, which generates the lowest traffic load level by uploading 10Kbyte every 15seconds per UE, has the highest variation in terms of the highest COV value. The main reason is that with less traffic demands either by less number of UEs or lower traffic loads requested by the UE, there are more resources to allocate for each UE and as a result there is more chance for the user to get PAG for transmissions. The use of PAG grants leads to an abrupt increase of the data rate, which causes a higher variation. Since the maximum number of PAG users is limited in the system, therefore when the number of users increases, the probability of getting the PAG is less for each UE and in turn the COV of cell throughput reduces. Appendix COV of Cell Throughput 1M 15s 3GPP FTP 100K 15s 50K 15s 10K 15s Number of UEs Figure A.17: COV of cell throughput Following discusses the impact of different traffic models, and number of UEs in the cell on the lub dimensioning results. Figure A.18 shows the lub dimensioning results for achieving 95% of the overall average application throughput in term o