Spectrum sensing performance of p-norm detector in random network interference
Bibliographic record
Abstract
Spectrum sensing performance of a cognitive radio (CR) deploying the traditional energy detector (ED) degrades in the presence of random network interference where both the number and locations of the interferers are random, thus preventing correct detection of primary user (PU) in the band of interest. However, it is not clear how the ED performance in such random network interference can be improved. Moreover, the previous studies do not consider complete modeling of the wireless environment including the cumulative effects of path-loss, fading and random network interference. We thus take these effects into account and investigate the performance of the p-norm detector, which offers the flexibility of adapting p to the operating conditions (as against fixed p = 2 for ED). Such adaptability yields remarkable performance gains over ED (say, 15% gain even at 10 dB lower (than that for ED) PU signal powers). Further, cooperative spectrum sensing with multiple CRs yields additional performance gains (say, 30% better performance at optimal cooperative detection threshold) compared to single CR based sensing even under the cumulative effects of path-loss, fading and random network interference.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".