Performance Analysis of Relay-Based Cooperative Spectrum Sensing in Cognitive Radio Networks Over Non-Identical Nakagami-<named-content content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="TeX">$m$</tex-math></inline-formula></named-content> Channels
Bibliographic record
Abstract
This paper provides performance analysis of relay-based cognitive radio (CR) networks and presents a detect-amplify-and-forward (DAF) relaying strategy for cooperative spectrum sensing over non-identical Nakagami-m fading channels. An advanced statistical approach is introduced to derive new exact closed-form expressions for average false alarm probability and average detection probability. We also introduce a novel approximation to alleviate the computational complexity of the proposed models. This paper points out the inconsistency of several assumptions that are typically used for performance analysis of CR networks and reveals that channel fading on the relaying links yields similar performance degradations as on the sensing channel. The study also shows that it is not necessary to incorporate all CRs in the cooperative process and that a small number of reliable radios are enough to achieve practical detection level. Compared with the amplify-and-forward strategy, refraining the heavily faded relays in the DAF strategy improves the detection accuracy and reduces the bandwidth requirement of the relaying links. The presented analysis could lead to intuitive system design guidelines for CR networks impaired with non-identical faded channels.
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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 | 0.005 |
| 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 | 0.000 |
| 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".