Resource allocation and scheduling schemes for WCDMA downlinks
Bibliographic record
Abstract
We analytically derive the appropriate rates and optimum transmit power levels that need to be allocated for high data rate services in downlinks of a WCDMA system with a time-slotted structure consisting of variable-length time slots and frames. It is shown that the average throughput decreases by 90% and average delay increases ten-fold in a severe shadowing environment (/spl sigma/=8 dB) compared to no shadowing. However, by introducing an outage probability of 0.05 as opposed to serving all the mobiles, the average system throughput can be increased six-fold and the average delay can be reduced by about 80% in such channel conditions. We analyze the trade-off between the throughput and delay performance of the system and the operating point of the outage. We consider two modes of transmission: a uni-access mode in which only one user is allowed to access the channel at a time, and a multi-access mode in which multiple users are allowed. We compare the performance of four scheduling schemes such as round-robin and fastest-first schemes that are appropriate for the uni-access mode, and equal-rate and equal-weight schemes that are for multi-access mode transmission. Simulation results show that the system employing the uni-access mode schemes performs better than one with multi-access mode schemes in terms of the average delay, and performs worse in terms of fair allocation of data rates.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".