ETSI/DECT-2020 Component RIT Technology Implementation and Evaluation Contributions in mMTC and URLLC Use Cases
Bibliographic record
Abstract
We propose a comprehensive evaluation of ETSI/DECT-2020 New Radio (NR) as an IMT-2020 candidate technology for the mMTC (connection density) and URLLC (reliability) use cases. Several independent evaluation groups assessed DECT-2020 NR performance against the IMT-2020 requirements and INRS played a key role within the Canadian Evaluation Group (CEG) in evaluating the technology via simulation. In order to develop 5G link- and system-level simulators, it is necessary to identify and understand all key characteristics of DECT technology. Nevertheless, the mMTC use case offers D2D communications, which made simulation analysis extremely cumbersome. In this paper, we also explain the different maneuvers in order to fasten the simulations and reduce complexity while respecting all DECT specifications. Simulation results provided from link- and system-level simulations for both URLLC and mMTC use cases show that DECT-2020 component RIT meets the IMT-2020 requirements.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".