Non-Orthogonal Multiple Access with Wireless Caching for 5G-Enabled\n Vehicular Networks
Bibliographic record
Abstract
The proliferation of connected vehicles along with the high demand for rich\nmultimedia services constitute key challenges for the emerging 5G-enabled\nvehicular networks. These challenges include, but are not limited to, high\nspectral efficiency and low latency requirements. Recently, the integration of\ncache-enabled networks with non-orthogonal multiple access (NOMA) has been\nshown to reduce the content delivery time and traffic congestion in wireless\nnetworks. Ac-cordingly, in this article, we envisage cache-aided NOMA as a\ntechnology facilitator for 5G-enabled vehicular networks. In particular, we\npresent a cache-aided NOMA architecture, which can address some of the\naforementioned challenges in these networks. We demonstrate that the spectral\nefficiency gain of the proposed architecture, which depends largely on the\ncached contents, significantly outperforms that of conventional vehicular\nnetworks. Finally, we provide deep insights into the challenges, opportunities,\nand future research trends that will enable the practical realization of\ncache-aided NOMA in 5G-enabled vehicular networks.\n
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".