Performance evaluation of DS/CDMA systems employing adaptive transmission rate under imperfect power control
Bibliographic record
Abstract
Power control is essential for CDMA systems to increase the capacity. Power control based on equalizing the received power levels from different users was proposed. Perfect power control is hard to achieve for high mobility users. The error in the received signal is usually modeled as a lognormal variable with a standard deviation that is a function of the mobile's velocity. In a previous work, we have shown that this standard deviation is also a function of whether or not the mobile is communicating with the base station where the power is measured. In this work, we use the error statistics to model the intercell interference. We also employ adaptive rate transmission where the data transmission rate is a function of the number of users in the system and the errors in the power of the received signals. We show that the adaptive rate scheme helps to reduce the blocking probability and the average service time for light traffic conditions. However, for heavy traffic, users reduce their transmission rate and start to accumulate in the system making its performance similar to the constant rate system. Finally, we investigate the effect of imperfect power control on such an adaptive rate scheme.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".