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Maximum rate or conventional scheduling maximizes the sum capacity of the system at the expense of fairness among users, whereas proportional fair user selection scheme satisfies fairness among users at the expense of system sum-rate [44, 45].
In summary, the opportunistic and even the generalized order user selection schemes are suitable for systems where the system overall sum-rate capacity or the overall performance is the main requirement of the system; conversely, the proportional fair user scheduling scheme is more desirable in systems where fairness among users is the first priority.
The fair user demand allocation scheme assigns users' demand to MRs solely based on the nearest neighborhood scheme, without considering the locations of MRs relative to the IGW.
6,7,8 and 9. Table 2 also shows that CMRP-1 outperforms CMRP-1/Fair when the users' demand becomes large, which substantiates that WLA performs better than that of the fair user allocation scheme.
In order to show the merit of CMRP-1 and CMRP-2, we first compare the simulation results of the CMRP framework with various existing schemes, such as the fair user demand allocation, the shortest path routing, and a greedy MR selection scheme, denoted as CMRP-1/Fair, CMRP-1/SP, and CMRP/Greedy, respectively.
As an example on the suitability of the scheme to be used, although the proportional fair user scheduling could be helpful for users of weak channels, the loss that occurs in throughput when this scheduling scheme is used can be large in situations where users are scattered across the cell [46].
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The construction and evaluation of the instrument enabled us to create a fair, user-friendly, short, and exact test of conceptual knowledge (fair measurement and efficiency) that is adapted to the content taught to secondary school students (content validity at secondary school level) and that can be validly applied both as a pre- and posttest to reflect learning progress (valid change measure).
This ensures everyone's safety and also keeps the idiots away who come to leech off fair users like myself.
The company says it is not fair for average users to subsidize heavy users.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com