PEP Analysis of AF Relay NOMA Systems Employing Order Statistics of Cascaded Channels
Bibliographic record
Abstract
The precise error performance analysis is challenging for non-orthogonal multiple access (NOMA) systems due to nonlinear successive interference cancellation (SIC) processing among NOMA users. In this paper, the pairwise error probability (PEP) performance of different users is investigated for relay NOMA simultaneous wireless information and power transfer (SWIPT) systems. By employing the order statistics theory, we obtain the ordered probability density function of the cascaded channel through Source-to-Relay-to-User links. Then we derive the analytical closed-form PEP expressions for NOMA users. To obtain the approximate closed-form PEP, we explore the finite series representation of the power of the modified Bessel function to replace the integrand terms. Monte Carlo simulation results show that the approximate analytical PEP of each user is basically in agreement with the simulated PEP. Furthermore, on the basis of the closed-form PEP, the influence of relevant system parameters on the error performance is examined via numerical simulations, which manifests that the choice of power allocation coefficients should be balanced between the users’ channel conditions and the demanded quality of service.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| 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".