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Secondly, we present the results in a small 4 link scenario and compare the IA scheduler and PF scheduler performance to the optimum transmission schedule obtained by a centralized scheduler with global knowledge.
(iv) We evaluate the performance of IA scheduler in numerical examples, where IA scheduler is compared to both PF scheduler and the global optimum transmission schedule obtained by a centralized scheduler having full system-wide information.
Then a transmission schedule of th scheduler can be represented with variable, where is the number of resource blocks and is the number of links served by th scheduler.
In the simulations, we compare IA scheduler to PF scheduler as well as to the optimum transmission schedule given by a centralized scheduler with full knowledge of interference channels.
The event of a single scheduler updating its transmission schedule such that the system utility increases is denoted by.
Ideal IA scheduler converges to a stable transmission schedule.
The above states that IA scheduler converges to a local optimum transmission schedule in the case of perfect channel estimates and all IA messages being heard.
This problem can be overcome by randomization whereby in each frame the schedulers make a random decision of whether to repeat the transmission schedule from the previous frame (persist) or to update the schedule.
The DOPC system achieves harmonic streaming transmission using the proposed differentiated transmission schedule strategy (DTS2).
At times, Enron filed a transmission schedule that would have the effect of reducing congestion, with the power flow going through the off-line hubs.
The BTB-TDMA determines transmission schedule of UNs based on UNs' estimated propagation delays (EPDs), reflecting on UNs' mobility.
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