Frequency-time scheduling algorithm for OFDMA systems
Bibliographic record
Abstract
Frequency-time scheduling is an essential radio resource management (RRM) function in Orthogonal Frequency Division Multiple Access (OFDMA) wireless systems. In the literature, there are several OFDMA scheduling algorithms such as the Hungarian and Max-Max algorithm. However, such algorithms do not consider the multiuser diversity into account. In this paper, we propose a scheduling algorithm that exploits the multiuser diversity in both time and frequency domains. Also, the proposed algorithm utilizes the Proportional Fairness (PF) criterion to achieve fairness among users in the system. In order to support multimedia bursty traffic, our algorithm allows more than one user to share a subband in each time frame. The proposed algorithm iteratively assigns the available subbands to be shared among different users concurrently. We compare the performance of the proposed algorithm with other OFDMA scheduling algorithms in the literature. Results show that the proposed algorithm outperforms other algorithms in terms of the throughput with comparable fairness performance.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".