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Figure 10 Comparison of resource management schemes in relay-enhanced system, high load scenario (a-b).
High load scenario: Relay-less system: 95% to 100% load Relay-enhanced system: 80% load.
This approach results in higher average GBR satisfaction level (88% in the high load scenario) and improved long-term PDR statistics (on average 5.5% PDR in the low load scenario and 17.5% PDR in the high load scenario).
The users' rates were obtained under the assumption that, a high load scenario that constitutes an upper bound.
In a high load scenario, the probability that the buffer is not empty approaches one, that is,.
The approach, however, does not consider directly the data rate requirement, thus it provides relatively low GBR satisfaction (on average 84% in the high load scenario).
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Studies show that WAVE can prioritize messages; however, in dense and high load scenarios the throughput decreases and the delay increases significantly.
The proposed system performed the best under high load scenarios where it reduced the number of connections the most, where individual users' throughput can be improved by more than 60%.
Furthermore, the high loading scenario could be useful with a wash-free consolidated pretreatment-saccharification process using recently developed IL-tolerant enzymes [ 35].
Fig. 7 Provisioned transport capacity versus number of cells for a low-load scenario and b high-load scenario Fig. 8 Statistical multiplexing gains for high-load scenario.
We give the capacity of up to 1000 cells, which we consider to be the approximate order of magnitude for the number of cells in a single large city.3 In addition, the corresponding multiplexing gains for the high-load scenario are given in Fig. 8.
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