A Fair Scheduling in 5G RAN Using Q-Learning
Bibliographic record
Abstract
The emergence of resource-critical applications (e.g., autonomous vehicles, AR/VR) spurs the deformation of telecommunication networks like 5G/6G. Specifically, efficient radio resource allocation and management are the cornerstones of these applications, which we can achieve through smart spectrum scheduling. Reinforcement Learning (RL) takes the lead in this space due to the ability to dynamically adapt to the changes in environments without requiring data labelling or collecting a large volume of samples. Existing RL-based schedulings, however, fail to effectively incorporate key performance metrics in their learning environment for a trustworthy resource allocation and are also computationally intensive. Thus, we propose a Q-learning-based downlink scheduling scheme incorporating weighted reward to balance between multiple performance metrics without demanding significant resources. Extensive evaluations confirm that our algorithm can improve the cell throughput and packet delay by around 43% and 32%, respectively, compared to the state-of-the-art scheduler.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".