Rate allocation mechanisms for multi-class service transmission over cognitive radio networks
Bibliographic record
Abstract
In this paper, we study rate allocation mechanisms that allocate available transmission rate of a particular cognitive radio (CR) user among its different class of services. In particular, we formulate the rate allocation mechanism of a CR user between its two different class of services namely, delay sensitive (DS) and best effort (BE) services as a Markov decision process. Then the optimal rate allocation mechanism that minimizes the average queuing delay of DS service while guaranteeing the packet loss probabilities of both class of services is obtained using a linear programming technique. Since the optimal rate allocation mechanism can be complex to implement in practice, we study a low-complexity suboptimal rate allocation mechanism. For this suboptimal scheme, we develop a queuing analytic model in order to measure different quality parameters. Selected numerical results show that the performance of suboptimal rate allocation mechanism is quite similar to the optimal rate allocation mechanism for the considered system parameters. The developed queuing analytic model is also useful for call admission controller design when the suboptimal scheme is employed.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".