Directional Power Control of 5G Radio Base Stations for EMF Compliance—Part I: Design Principles and Feature Validation
Bibliographic record
Abstract
When the electromagnetic field (EMF) compliance boundary of a radio base station (RBS) is determined based on the actual maximum EMF exposure condition according to the international standard IEC 62232:2022, software features to monitor and control the time-averaged transmitted power or the equivalent isotropically radiated power (EIRP) are recommended or required in many countries. In this publication, a new software feature that maps the EIRP contributions from massive multiple-input multiple-output (MIMO) RBS to eight azimuthal segments within a cell is presented. The power control is much less often triggered using the new feature than a previous one based on the calculation of the transmitted power only. Furthermore, operators can set the threshold per segment by using the presented feature. The counters associated with the feature provide a conservative estimate of the EIRP in all segments. This publication is divided into two parts. Part I describes the design principles of the new feature and feature validation tests based on in-situ measurements. The results confirm that the new feature ensures that the time-averaged EIRP is maintained below the set threshold in all test cases. Part II describes the time-synchronized measurements in a live 5G site and a network-level deployment by comparing the new directional feature with the cell-wide feature. The results confirm that the new feature has less impact on the network performance.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".