Performance Analysis of Co-Operative Beacon Sensing Strategies for Spatially Random Cognitive Users
Bibliographic record
Abstract
Primary user (PU) beacons must be detected by cognitive users (CUs) to access spectrum holes, and misdetection results in interference on PUs. To alleviate this problem, sensing results of spatially separated CUs can be combined to make a final decision. In this paper, we analyze several such co-operative beacon sensing (CBS) strategies given spatial randomness of CU and PU nodes, which is modeled via independent homogeneous Poisson point processes. We consider two cases of beacon emitter placement: 1) at PU-transmitters and 2) at PU-receivers. We analyze three separate local beacon detection schemes and propose five CBS schemes. They require the sharing of CU results via a control channel subject to Rayleigh fading and path loss, and making a final decision via the OR rule. By using stochastic geometry, we derive both the misdetection probability, the false alarm probability, and the primary outage and show that impressive gains are achievable. For example, with PU-receiver beacons, CBS reduces misdetection by a factor of 104. In contrast, with PU-transmitter beacons, the reduction diminishes with the increased cell radii, but there exists an optimum cooperation radius.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".