Scheduling schemes for multimedia service in wireless OFDM systems
Bibliographic record
Abstract
Scheduling schemes play a key role in the system performance of broadband wireless systems such as WLANs/WMANs. Maximal SNR and round robin are two conventional scheduling strategies that emphasize efficiency and fairness, respectively. The proportional fair scheme provides a trade-off between efficiency and fairness, and has been well studied in TDMA and CDMA systems. In this article we extended the PF scheduling scheme to OFDM-based BWSs (OPF). In addition, we propose three variations: adaptive OPF (AOPF), multimedia AOPF (MAOPF), and normalized MAOPF (NMAOPF) in order to meet the QoS requirements for multirate services in multimedia systems. The adaptive modulation and coding schemes in time varying and frequency selective fading are considered. The system performances of the algorithms are compared in terms of efficiency (throughput and mean packet delay) and fairness (user satisfaction rate and average user rate). Joint physical and media access control layer simulation results show that AOPF and MAOPF can improve throughput at the cost of fairness, and NMAOPF can provide the highest throughput without losing fairness.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".