A combined power/rate control scheme for data transmission over a DS/CDMA system
Bibliographic record
Abstract
Power control is essential for spread spectrum cellular systems. Perfect power control can result in high intercell interference due to tracking of deep fades. Limiting the user transmitted power will not solve this problem because this will suppress both the interference and the signal itself. We investigate the performance of data users that can tolerate some delay in their transmission. We limit the increase in power to compensate for multipath fading by P/sub l/(dB) and get the extra gain required by reducing the transmission rate. We show that this can result in about a 231% increase in the total transmission rate when the multipath fading consists of a single Rayleigh path. A major issue in this scheme will be tracking the transmission rate by the base station to use it in the demodulation process which is a difficult task when the gain commands can be in error. In this case we propose using only four different transmission rates (R/sub m/, R/sub m//2, R/sub m//4, R/sub m//8) and the base station will demodulate the received signal four times for the four different rates and then pick one data sequence based on metrics like the CRC pass/fail. We show that in such a case, the increase in the total transmission rate can reach 187%.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".