Laboratory performance assessment for DRM+ system in single and dual-antenna transmission scenarios
Bibliographic record
Abstract
It has been shown that due to its smaller signal bandwidth (96 KHz), DRM+ signal could suffer from flat fading degradation at low receiver speeds, particularly in urban environments. To alleviate this problem, a simple transmit delay diversity (TDD) has been proposed in the standard. This paper tries to investigate this issue by presenting the performance results of multipath fading characterizations performed in the laboratory for DRM+ system. Both single and dual-antenna transmission scenarios have been considered. By using a sophisticated hardware channel simulator, different radio channel models corresponding to various scenarios and environments (urban, suburban, mountain, SFN, etc) could be tested and compared. For a single antenna scenario, the results show that indeed, at low receiver speeds (5 km/h), the DRM+ receiver was unable to cope with the effect of flat fading in the urban channel model. However, in the same environment, TDD scheme provides 3 dB gain over single antenna transmission if low or moderate correlation level is maintained between the two signal paths. Moreover, it has been found that for a TDD enhanced DRM+ system, a better performance is obtained with the SFN channel model as compared to urban and rural channels when the two signals are highly correlated.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".