SWIPT-Enabled Cooperative NOMA With <i>m</i>th Best Relay Selection
Bibliographic record
Abstract
Non-orthogonal multiple access (NOMA) was recently regarded as a potential technique for next generation wireless networks. Recent works on relay selection for cooperative NOMA systems have mainly addressed the best relay selection to forward its received signals to terminal nodes. Nonetheless, in practical scenarios, the best relay may be unavailable due to non-ideal conditions such as scheduling and overload constraints or possibly due to channel feedback delay. Therefore, there is compelling need to consider a more practical solution, in which the best available relay is selected. In this article, we examine the error rate performance for simultaneous wireless information and power transfer (SWIPT)-enabled NOMA, while considering the selection of the m th best available relay. In particular, we present an exact pairwise error probability (PEP) expression to obtain a bit error rate (BER) upper bound. The asymptotic PEP is investigated to evaluate the achievable diversity order for NOMA users. Finally, simulation results are provided to verify the accuracy of the derived PEP expressions and to give more insights into the system performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".