Predictive Channel Access in Cognitive Radio Networks Based on Variable Order Markov Models
Bibliographic record
Abstract
The concept of CR enables the unlicensed users to share the spectrum with licensed users, on the condition that the licensed users have preemptive priority. The use of the channel by unlicensed users should not result in more than acceptable interference level to the licensed users, if interference occurs. The sense and react strategy by unlicensed users sometimes does not lead to acceptable level of interference while maintaining an acceptable data transfer rate for the unlicensed users. In this paper we introduce a predictive channel usage scheme which is capable of reducing the interference caused by the unlicensed users. Furthermore our scheme is capable of increasing the data rates the unlicensed users experience through the reduction of the idle channel identification delay. In our scheme no assumptions are made about the distribution of licensed user channel usage. We learn the traffic characteristics of the channels using a learning scheme called Probabilistic Suffix Tree (PST) algorithm.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".