Opportunistic Adaptive Non-Orthogonal Multiple Access in Multiuser Wireless Systems: Probabilistic User Scheduling and Performance Analysis
Bibliographic record
Abstract
This paper designs a novel opportunistic adaptive non-orthogonal multiple access (OA-NOMA) strategy, where a base station (BS) employs NOMA to serve a near user (NU)-far user (FU) pair opportunistically scheduled from M NUs and K FUs. In particular, the NOMA transmission to the scheduled NU-FU pair adaptively operates in one of two modes: Direct NOMA mode, in which the BS directly serves the scheduled NU-FU pair with using NOMA; Cooperative NOMA mode, in which the scheduled NU receives the messages intended by both scheduled users from the BS, and then forwards the message intended by the scheduled FU. For the OA-NOMA strategy, a scheduling candidate acquisition method and a probabilistic user pair scheduling scheme are proposed to guarantee the transmission reliability and improve the scheduling fairness, respectively. To evaluate the scheduling fairness, we develop a max-min fairness criterion and show that the OA-NOMA strategy approximately achieves max-min fairness. The reliability of the OA-NOMA strategy is also evaluated in terms of outage probability and diversity order. For the outage probability, we derive an approximate expression and numerically verify its tightness. For the diversity order, we show that the proposed OA-NOMA strategy achieves a diversity order of M.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".