Opportunistic Non-Orthogonal Multiple Access Scheme with Unreliable Wireless Backhauls
Bibliographic record
Abstract
The demand for increased connectivity and reliability of devices in the fifth generation (5G) of wireless communications requires new technology for ensuring massive connectivity and high spectral efficiency. In addition, wireless backhauls with guaranteed reliability are being considered to improve the overall system performance. In this paper, we investigate an opportunistic non-orthogonal multiple access (NOMA) system with unreliable wireless backhauls. In particular, we develop two opportunistic selection rules which allow the selection of the best among either near or far-away group transmitters, considering both the unreliability of wireless backhauls and fading effects of fronthauls. In order to analyze the performance, new exact and approximated closed-form expressions for the outage probabilities of the grouped receivers are derived, thus providing an insight into the impact of unreliable random backhauls and opportunistic NOMA. We show that the proposed scheme gives an outage performance gain of more than 3dB gains to a dominant receiver in the selection rules and improvement in receiver fairness when compared to the orthogonal multiple access (OMA) with an unreliable wireless backhaul. In addition, our results clearly reveal that unreliability levels of wireless backhaul links are responsible for the outage floors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".