Rate allocation mechanisms for multi-class service transmission over cognitive radio networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it