Link adaptation and power control for streaming services in EGPRS wireless networks
Bibliographic record
Abstract
Using the MPEG-4 advanced audio coder (AAC) music as an example of streaming applications, we investigate the improvement of error performance for the streaming service by link adaptation and power control techniques in an enhanced general packet radio services (EGPRS) cellular network. A low packet error rate and variability are essential in providing a short error-burst length so that error concealment techniques can be effectively applied to music packets. We study the effects of a combined link adaptation and power control scheme (referred to as the error-based scheme) for achieving a target error rate and reducing error variability. By simulation, we compare the error performance of the error-based scheme at both the EGPRS block and AAC frame level with another adaptation algorithm (referred to as the throughput-based scheme) with a goal of maximizing overall network throughput. It is found that when offered with a similar traffic load, the former scheme can provide noticeable improvement of music quality over the throughput-based scheme. To achieve a similar AAC frame error rate, our results also show that the error-based scheme can increase the link throughput over the throughput-based scheme by 66.7% in one of our examples. These results reveal that by aiming at required error targets and thus reducing error variability, the error-based scheme for link adaptation and power control are helpful in improving quality and capacity for streaming applications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".