Impact of Beacon Misdetection on Aggregate Interference for Hybrid Underlay-Interweave Networks
Bibliographic record
Abstract
The impact of beacon misdetection on the aggregate interference from a hybrid underlay-interweave network is analyzed, for a Poisson field of cognitive radio (CR) nodes distributed over an annular region. This network consists of two types of nodes: underlay, and interweave. The underlay nodes are allowed to transmit anytime, whereas the interweave nodes must first sense an out-of-band beacon. When this sensing is erroneous, interweave node transmissions increase the interference. We analyze the interference statistics by deriving the exact moment generating function, the mean, and the outage probability of the primary receiver, for path loss and Rayleigh fading. Our analysis suggests that hybrid underlay-interweave CR systems are more suitable for areas with low path loss exponents such as rural/suburban environments.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".