Uplink scheduling solution for enhancing throughput and fairness in relayed long‐term evolution networks
Bibliographic record
Abstract
Relaying is one of the key techniques adopted by third‐generation partnership project long‐term evolution (LTE) advanced as part of 4G cellular technologies, aiming to increase coverage and capacity of networks especially for the edge nodes. The authors have considered the uplink scheduling of LTE networks with the help of positioned relay nodes which have fixed routing configuration. The entire problem is projected as a constrained convex optimisation formulation and for the solution purpose, subgradient method is adopted. Revealing the guiding principles of optimal solution, a few suboptimal scheduling algorithms are proposed to allocate resource blocks across all nodes with the help of existing work. Deploying a large number of relays may not be useful to basic user nodes, and hence, the proposed schemes are adaptive and have ability to distinguish useful relays from not‐useful ones. In addition to system throughput maximisation, for ensuring fairness across user nodes, the authors have proposed scheduling techniques which are the outcome of Nash bargaining solution. Numerical calculations and results have been shown to justify that relay nodes can potentially improve system's performance at low load, whereas at high load they remain inactive because of their inability to contribute.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".