Joint admission control and resource allocation with GoS and QoS in LTE uplink
Bibliographic record
Abstract
In this paper, an admission control (AC) scheme is proposed for handling multiclass Grade of Service (GoS) and Quality of Service (QoS) in Uplink Long Term Evolution (LTE) systems. GoS requirement in conjunction with QoS has been seldom taken into account in previous admission control and resource allocation algorithms for LTE uplink. We propose a novel algorithm for handling the priorities while fulfilling the QoS objective of all granted requests. It corresponds to a solution that combines resource allocation and admission control properties to satisfy the GoS and QoS objectives. Call blocking probability, call outage probability, system capacity and number of effectively served requests are used as performance metrics. Numerical results show that it is possible to manage a priority scheme which satisfies the QoS constraints of all granted requests without any system capacity loss, when comparing to previous algorithms. Furthermore, the proposed AC algorithm gain, for the most sensitive traffic, can be around 20% over the reference AC algorithm.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".