Closed-Form Analysis of Relay-Based Cognitive Radio Networks Over Nakagami- $m$ Fading Channels
Bibliographic record
Abstract
We propose a general framework for a comprehensive performance analysis of cooperative spectrum sensing (CSS) in cognitive radio (CR) networks. Specifically, we investigate the detection accuracy of a relay-based CR network over independent nonidentical Nakagami- m fading channels. Based on the probability density function (pdf) approach, we derive new exact and approximated closed-form expressions for the average detection probability and the average false alarm probability employing two diversity combining techniques, namely, the maximal ratio combining (MRC) scheme and the selection combining (SC) scheme. We also investigate the convergence rate of infinite series that appears in the derived exact closed-form expressions and propose to use a powerful acceleration algorithm that allows for the series termination with a finite number of terms. The results obtained reveal the importance of including the relaying link statistics and the combination techniques in the performance analysis of CR networks. The derived closed-form expression can be used to determine the energy threshold and the relaying power constraint that meet a given detection accuracy value over nonidentically distributed Nakagami- m fading.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".