Design framework and suitability assessment proposal for 5G air interface candidates
Bibliographic record
Abstract
This paper proposes a unified way of describing 5G air interface (AI) design proposals using a 5G service/frequency map based on work carried out as part of 5G-PPP/H2020 project “METIS-II”. It then crucially proposes a design framework and suitability assessment process for 5G AI candidates. The proposed assessment methodology focuses on “harmonization Key Performance Indicators, or KPIs” and how to measure them (qualitatively / quantitatively). The paper proposes that evaluation of 5G AI candidates should, in addition to performance, include the “extent of harmonization”, which is defined in this paper. The case is argued that these harmonization KPIs are essential when assessing new 5G AI technologies. Additionally, an initial overview of different User Plane aggregation approaches is provided. We then discuss the types of Application Program Interfaces (APIs) which may need to be offered to higher layers, as well as a broad set of 5G Control Plane features and how AI considerations could take these into account.
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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 | 0.024 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".