Hierarchical Channel Recovery for Heterogeneous Cognitive Radio Networks
Bibliographic record
Abstract
Cognitive radio networks (CRNs) benefit from several features, such as decision-making, spectrum-awareness and reconfigurability over heterogeneous channels. After a link failure due to the appearance of primary users or if the channel quality becomes unacceptable, these features enable CRNs to perform a spectrum migration to a new channel to recover the transmission. In this paper, we propose a general hierarchical recovery model in which the heterogeneous channels are classified based on their parameters into distinct sets. Instead of performing a flat channel search over all channels, the CRN first selects a channel set and then performs a spectrum migration over the selected set to complete the channel search. The focus of this research is the decision procedure for selecting the best channel set to perform the recovery mechanism based on the CRNs current state and its knowledge concerning the different channel parameters. A general dynamic decision model is proposed to find the optimal solution to this problem. Additionally, we present three decision-making heuristic algorithms. Numerical and simulation results are provided to illustrate the significant benefits of the optimal and heuristic decision algorithms for channel recovery over heterogeneous channels.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".