Proportional Fairness-based Joint Channel and Power Allocation for Hybrid NOMA-OMA Downlink Systems
Bibliographic record
Abstract
This work considers a downlink hybrid NOMAOMA multiuser transmission system that divides users into pairs, each pair sharing one channel using Non-Orthogonal Multiple Access (NOMA), while different pairs are assigned orthogonal channels using Orthogonal Multiple Access (OMA). To achieve high system efficiency while guaranteeing fairness, we propose a joint power and channel allocation framework with the proportional fairness objective, which maximizes the sum of logarithmic rates. The problem is decoupled into the power allocation (PA) and channel assignment (CA) subproblems, which are solved iteratively. Our main contribution is proposing a globally optimal solution algorithm for the CA subproblem, which is obtained by casting the problem as a bipartite graph matching problem. We show empirically that the proposed joint PA-CA solution performs very close to the exhaustive search for small numbers of users. Extensive experiments demonstrate that the proposed framework significantly outperforms several benchmark schemes in both system efficiency and fairness. Index Terms-NOMA, joint channel and power allocation, proportional fairness, sum of logarithmic rates, bipartite graph matching.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".