5G System Level Simulation Calibration Using MATLAB 5G Toolbox
Bibliographic record
Abstract
MATLAB is one of the most widely used simulation platforms for academia and research. It contains a powerful 5G toolbox for performing both link-level and system-level simulations. As far as we are aware, the 5G Toolbox implementation is incomplete for system-level simulations that would comply with 3GPP assumptions. We modify the toolbox to make it compatible with 3GPP calibration simulation scenarios. Simulation results of the Rural-eMBB and Urban Macro-mMTC scenarios show that the resulting SINR falls within 1 dB from the 3GPP calibration average, well within the tolerance margin of 1~2 dB, suggesting the 5G toolbox is a suitable platform for 5G system-level simulations. One downside to the toolbox is its long execution time, which makes testing and developing very time-consuming. Currently, we are working on abstracting some of the link-level features to reduce the complexity. We also plan to incorporate multicast and broadcast transmission as well as layered division multiplexing into the toolbox. Once completed, the new features will be packaged as plug-in functions to the 5G Toolbox, and will be open-source, available for interested research groups.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".