Efficient Resource Allocation for OFDMA Multicast Systems With Spectrum-Sharing Control
Bibliographic record
Abstract
This paper considers the important problem of efficient allocation of available resources (such as radio spectrum and power) in orthogonal frequency-division multiple-access (OFDMA)-based multicast wireless systems. Taking the maximization of system throughput as the design objective, three novel efficient resource-allocation schemes with reduced computational complexity are proposed under constraints on total bandwidth and transmitted power at the base station (BS). Distinct from existing approaches in the literature, our formulation and solution methods also provide an effective and flexible means to share the available radio spectrum among multicast groups by guaranteeing minimum numbers of subcarriers to be assigned to individual groups. The first two proposed schemes are based on the separate optimization of subcarriers and power, where subcarriers are assigned with the assumption of uniform power distribution, followed by water filling of the total available transmitted power over the determined subcarrier allocation. In the third scheme, which is essentially a modified genetic algorithm (GA), each individual of the entire population represents a subcarrier assignment, whose fitness value is the system sum rate computed on the basis of the power water-filling procedure. Numerical results show that with a flexible spectrum-sharing control mechanism, the proposed designs are able to more flexibly and fairly distribute the total available bandwidth among multicast groups and, at the same time, achieve a high system throughput.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".