A greedy channel restoration scheme for opportunistic spectrum access
Bibliographic record
Abstract
Cognitive radios (CR) are an efficient approach to deploy a secondary network in the vacant portions of the spectrum licensed to primary networks. However, when the quality of the channel being used by a CR user is degraded due to environmental impacts or the return of primary users, the CR should vacate the channel and start a restoration process by searching and sensing other channels. The restoration process objective is to find the best channel in the shortest time to increase the throughput of the CR. We propose in this paper a greedy and intelligent restoration scheme which is triggered in any sensing period, not only when the quality of the channel is lower than a threshold value. Based on the state of the current operating channel, our restoration scheme calculates the optimal number of channels to be sensed in this sensing period and this number is dynamically updated based on the sensing results. This scheme is applied to a new multi-state Markov chain channel model which considers both the appearance of primary users and channel quality degradation. We show that it provides improvements for the CR throughput, compared to the other restoration schemes in the literature that consider a fixed SNR threshold (channel state) to trigger the restoration.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".