On the capacity of cellular DS/CDMA systems under slow Rician/Rayleigh-fading channels
Bibliographic record
Abstract
Power control is essential for code-division multiple-access (CDMA) cellular systems to overcome the near-far problem. Fast power control tracks multipath fading perfectly which increases the intercell interference and thus reduces the capacity. If there is a line of sight (LOS) component between the mobile and the base station with which it is communicating, the fading will be of Rician type and less deep fades will be encountered which reduces the intercell interference. In this paper, we investigate the system capacity assuming the fading between a mobile and the base station with which it is communicating to be Rician with a Rician factor K with the fading between the same mobile and other base stations to be Rayleigh. We show a large increase in the capacity even for small Rician factors. A Rician fading is usually encountered when the mobile is close to the base station and thus the path-loss exponent can be less than four. Thus, we investigate the effect of changing the path-loss exponent value on the system capacity. Finally, we reduce the intercell interference by imposing a limit on the maximum increase in power to compensate for multipath fading and show how can this increase the capacity by more than 100%.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".