A Proportional Fairness-based Power Allocation Scheme for Non-orthogonal Multiple Access Downlink Systems
Bibliographic record
Abstract
This paper tackles the problem of proportional fairness-based power allocation in downlink non-orthogonal multiple access (NOMA) systems. We address the scenario where the users are divided into groups of two. NOMA technique is applied within each group with the superposition coding at the transmitter side and successive interference cancellation (SIC) at the receivers’ side, and different groups are assigned separate channels. We assume that the grouping and the channel assignment are dynamically performed, and we optimize the power allocation among users by maximizing the sum of logarithmic rates with a constraint on the total transmit power. We prove that the globally optimal solution to the optimization problem can be obtained by solving the Karush-Kuhn-Tucker (KKT) conditions and propose an efficient algorithm for this purpose. Extensive experimental results demonstrate that the proposed scheme outperforms several existing NOMA schemes in terms of system throughput and/or fairness, achieving the best trade-off between these two criteria.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".