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By solving problem (1), both optimal slot assignments can be computed.
However, no WiMAX standards for optimal slot allocation have been defined.
The optimal slot size is Tc and SU's channel utilization equals to this channel's idle probability.
In the next Section, both algorithms (static-FSA and dynamic-FSA) are considered in order to propose an optimal slot distribution for the single channel environment.
Therefore, the optimal slot duration for minimum power can be found as τ mp ∗ = min τ ∈ 0, 1 P DF , (16).
Therefore, according to Theorem 5 of [11], we could obtain that the optimal slot size Ts ∈ (0, Tc] and SU's channel utilization is k × 1, which in accordance with SS-SA strategy.
Similar(51)
Furthermore, our proposed scheduler is the first that provides all near optimal slots' allocations.
When all the near-optimal slot allocations plans have been generated by the Pareto-search algorithm, they are forwarded to the Buffer-Tuning component.
We propose a novel approach that detects near-optimal slots' allocations in an efficient and fast way without requiring to enumerate all the possible plans.
We differ from these approaches in the fact that we examine a multi-objective optimization problem where the user submits multiple jobs and we want to provide her near-optimal slots' allocations.
We use a novel Pareto frontier greedy search algorithm for detecting near-optimal slots' allocations in a fast and scalable way, considering two different policies for traversing the frontier.
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