Joint Best Price-CQI Product Scheduling and Congestion Control for LTE
Bibliographic record
Abstract
In hybrid wired-wireless media, congestion control and scheduling are both trying to allocate bandwidth to the flows. Performance studies of long-term evolution (LTE) networks show that by decreasing the mismatch between scheduling at the base station of an LTE network and congestion control algorithms implemented at end users, significant improvements in the obtained overall throughput and fairness for best-effort traffic are achieved. In this paper, a problem formulation for joint scheduling and congestion control based on dual problem optimization is provided for a hybrid media, which includes a wireless LTE network. The original optimization problem is decomposed into two subproblems: congestion control problem and link scheduling problem. We propose methods to solve and implement these subproblems in different layers of a hybrid network, and call the derived link scheduling subproblem best price-channel quality indicator (CQI) product scheduling. Then, the performance of the proposed joint algorithm for LTE is studied. The results show a better tradeoff between the overall throughput and the fairness for the joint algorithm compared with classic cases in which congestion control and scheduling are separately designed and implemented without any cooperation. The behavior of the proposed best price-CQI product scheduler and a common proportional fair scheduler is compared, and some notable similarities in the case of applying logarithmic utility functions are observed.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".