Novel Scheduling Algorithms for Multimedia Service in OFDM Broadband Wireless Systems
Bibliographic record
Abstract
Scheduling algorithms play a key role in overall system performance of broadband wireless systems (BWS) such as WLAN/WMAN. Maximal SNR (MaxSNR) and Round Robin (RR) are two conventional scheduling strategies which emphasize efficiency and fairness respectively. Proportional Fair (PF) algorithm provides tradeoff between efficiency and fairness and it has been well studied in TDMA and CDMA systems. In this paper, we apply the PF scheduling algorithm to IEEE 802.16a OFDM based BWS and call it OPF. In addition, we propose three algorithms for multimedia services: (1) Adaptive OPF (AOPF), (2) Multimedia AOPF (MAOPF) and (3) Normalized MAOPF (NMAOPF). Adaptive modulation and coding schemes are applied to combat the time varying nature of the wireless channels. System performances of all six algorithms are compared in terms of efficiency and fairness.. Joint PHY and MAC layer simulation results show that the proposed schemes provide better tradeoff between efficiency and fairness than conventional algorithms.
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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 | 0.004 |
| 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 | 0.000 |
| 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".