Performance analysis of spectrum sharing‐based multi‐hop decode‐and‐forward over Nakagami‐ <i>m</i> fading channels subject to additive white generalised Gaussian noise
Bibliographic record
Abstract
In this work, the authors study the performance of decode‐and‐forward cognitive multi‐hop networks in an underlay spectrum sharing strategy for Nakagami‐ m fading channels with additive white generalised Gaussian noise. To this end, new exact analytical expressions for the average bit error rate of M ‐ary quadrature amplitude modulation ( M ‐QAM) and M ‐ary phase shift keying are derived and evaluated. In addition, an exact expression for the average symbol error rate of M ‐QAM is provided. Moreover, lower‐ and upper‐bound expressions for the ergodic capacity are obtained. Different scenarios are presented to study the influence of various key system parameters, such as fading severity, noise shaping parameter, and interference temperature, on the system performance. The obtained analytical results are supported with Monte Carlo simulations to confirm the accuracy of the analytical derivations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".