New Courteous Algorithm for Uplink Scheduling in LTE-Advanced and 5G Networks
Bibliographic record
Abstract
The fast evolution of the number of wireless users and the emergence of new multimedia services have motivated third-generation partnership project (3GPP) to develop new radio access technologies. Thus, the carrier aggregation (CA) was introduced from version 10 long-term evolution (LTE), known as long-term evolution-advanced (LTE-A), to meet the increasing demands in terms of throughput and bandwidth and to ensure the Quality of Service (QoS) for different classes of bearers in LTE networks. However, such solution stills inefficient until implementing good resources management scheme. Several scheduling mechanisms have been proposed in the literature, to guarantee the QoS of different classes of bearers in LTE-A and 5G networks. Nevertheless, most of them promote high-priority bearers. In this study, a new approach of uplink scheduling resources has been developed. It aims to ensure service fairness of different traffic classes that allocates bearers over LTE-A and 5G networks. Also, it raises the number of admitted users in the network by increasing the number of admitted bearers through a dynamic management of service priorities. In fact, the low-priority traffic classes, using low-priority bearers, are favoured during a specific time interval, based on the average waiting time for each class. Simulation results show that the QoS parameters were much improved for the low-priority classes without significantly affecting the QoS of high priority ones.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".