Interweave Cognitive Networks with Co-Operative Sensing
Bibliographic record
Abstract
This paper investigates the effects of different cooperative sensing strategies on erroneous spectrum sensing for an interweave cognitive radio network. The setup is as follows. Primary receiver nodes and secondary nodes are randomly distributed in R2. We model them as two independent homogeneous Poisson point processes. Beacon (out-of-band) signals, periodically transmitted by primary receivers, indicate to the secondary nodes that spectrum is occupied. Whenever beacon detection fails, the transmissions of secondary nodes generate harmful interference. Thus, to alleviate this issue, the misdetection probability of secondary nodes must be reduced. To this end, we propose two strategies: 1) a secondary node cooperates with its closest neighbour, and 2) a secondary node cooperates with M random neighbours within a given radius. Furthermore, along with these co-operation strategies, we investigate three primary beacon detection methods for secondary nodes: 1) separately decoding each primary beacon, 2) detecting only the closest primary receiver?s beacon, and 3) detecting the aggregate beacon signal from all primary receivers. For the exponential path loss and Rayleigh fading considered, we derive the total misdetection probability for each scheme along with the resulting outage probability of a primary receiver. We finally show through numerical results that M co-operation works better for lower reception thresholds, and that for a reception threshold of -120 dBm, a 104fold decrease in the misdetection probability is achievable.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".