Markovian‐based framework for cooperative channel selection in cognitive radio networks
Bibliographic record
Abstract
The authors propose Markovian‐based spectrum sensing policies in a cognitive radio system that leverages past sensing outcomes of several cooperating secondary users (SUs) to decide which channel (of primary users – PUs) should be sensed by each SU at a given time. These policies are based on a new finite‐state channel model that captures the fading condition as well as the occupancy state for each primary channel. The multiuser extension of this model is useful when multiple spatially distributed SUs share their sensing outcomes. The proposed schemes allow the asynchronous sensing outcomes obtained by the SUs over different slots to be fused together and converted into a posteriori probabilities for the current states of the primary channels. As the detection threshold in a spectrum detector balances the trade‐off between the false‐alarm and miss probabilities for detecting primary signals in a single primary channel, a design parameter is introduced to allow the system designer to devise policies with different levels of aggressiveness. The authors evaluate the optimality and complexity of the proposed sensing policies and show that our schemes significantly increase secondary use of the spectrum and/or reduce interference with PUs compared to a random selection policy or a cooperative sensing policy based on a two‐state channel model.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".