Power control and number of antenna elements in CDMA distributed antenna systems
Bibliographic record
Abstract
In this study the relationship between the number of antenna elements (AEs) in a CDMA distributed antenna (DA) system and the yielding reverse link SIR is investigated by taking the power control dynamic range into account. In environments hostile to propagation, perfect power control may not be realized with a central antenna (CA), because this would require an impractically high dynamic range. This situation may yield a significant decrease in capacity. In such environments, the DA system is an ideal solution, since as the number of antenna elements increases, the dynamic range of the power control decreases. It is demonstrated that by using a DA system with as small as 4 AEs, a capacity increase of almost 30% is achievable, compared to the CA type. However, in a single-cell system once there are a sufficient number of antenna elements to implement perfect power control within a reasonable dynamic range, there is no need for additional antenna elements. Also, in a multi-cell system with CAs, the occasional transmissions at very high power levels in order to maintain perfect power control cause significant intercell interference. Since with the DA such situations are almost eliminated, the intercell interference is kept at a minimal level. Therefore, the DA is an ideal antenna type for both singleand multi-cell systems employing CDMA modulation.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| 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".