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Therefore, non-real-time users are less insensitive to the withdrawal ratio than real-time users.
The withdrawal ratio γ is defined as the ratio of the mean number of withdrawal channels to the total number of leasehold channels in a cell.
From the figure, we also observe that the throughput of real-time users at γ=0.4 is higher than that at γ=0.8 because more free channels are available to real-time users at lower withdrawal ratio.
Table1 shows the numerical and simulation results in a cognitive network, in which the real-time and non-real-time thresholds are 30, the withdrawal ratio is 0.2 and system load is 1.
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Cognitive networks may suffer different withdrawal ratios which may result in different system performances.
Finally, we study the performances of real-time and non-real-time traffic at different loads with various withdrawal ratios.
In the following, we consider two withdrawal ratios, 0.4 and 0.8, in a cognitive network where the length of queue is 5.
The quality-of-service metrics at different withdrawal ratios satisfy our objective; that is, (i) the inter-cell hand-off and withdrawal dropping probabilities of real-time and non-real-time users are kept below 0.04, and (ii) the waiting time of non-real-time users are below 5 seconds.
On D4, 24 h after MA, the withdrawal latency ratio in the MA group was 0.96 ± 0.09; this remained significant compared with the ratio observed before MA on D3 (P < 0.05).
There were no significant differences in the withdrawal latency ratio among groups on D3 and D4.
On D3, the withdrawal latency ratio increased from 0.70 ± 0.05 before injection to 1.01 ± 0.08 after injection (P < 0.01; Mann Whitney rank sum test).
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Justyna Jupowicz-Kozak
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