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In particular, this article has evaluated if the properties and trends of utility-based scheduling under the full buffer model also hold when the finite buffer is applied.
Under the full buffer model, the CDFs of the user throughput for both α values cross at approximately the 60th percentile of the CDF.
Both scheduling algorithms are evaluated under the full buffer traffic model with the following average number of users in the cell N = [8, 11, 14, 17, 20].
As expected, under the full buffer model the gradient scheduling algorithm with α = 1 maximizes the average utility when the utility function is (21).
In addition, under the full buffer model, the gradient scheduler exhibits a trade-off between average user throughput and the user throughput at 5% outage.
Therefore, it is concluded that designs of scheduling algorithms for NRT services for OFDMA systems carried out under the full buffer model assumption may fail to provide the desired performance benefits in realistic scenarios.
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We consider that the results obtained under the finite buffer model are more generalizable than those obtained with the full buffer because the latter disregards the fact that in a realistic OFDMA network all users do not simultaneously request the download/upload of an infinite amount of data.
Section 4 presents the full buffer and the finite buffer traffic models as well as KPIs.
This section analyzes the sensitivity of the performance of the α-fair gradient scheduler to the network load under the finite and full buffer traffic models.
This section presents the performance of the α-fair gradient scheduling algorithm under the finite and full buffer traffic models for different α values (see (16)) assuming a fixed offered load.
Under full buffer traffic conditions, this RU metric will indicate that the cell load reaches 100%% and thus ignores the level of satisfaction/saturation which would be achieved if more users connect to the same cell.
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