Proportionally Fair Joint Power and Channel Allocation for Hybrid NOMA-OMA Downlink Systems
Bibliographic record
Abstract
We consider a downlink multiuser transmission system that divides users into clusters, each cluster shares one channel using non-orthogonal multiple access (NOMA), while different clusters are assigned orthogonal channels. To achieve high system efficiency while guaranteeing fairness, we propose a joint power allocation (PA) and channel allocation (CA) framework with the proportional fairness (PF) objective, which maximizes the sum of logarithmic rates. The problem is decoupled into the PA and CA subproblems, which are solved iteratively. For the PA subproblem, we prove that although it is not convex, strong duality holds and the problem can be solved globally optimally by solving the KKT conditions. We further propose a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(T(\text{log}\frac{1}{\epsilon})^{2})$</tex-math></inline-formula> time algorithm for this purpose, where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</i> is the number of users and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> is the tolerance threshold. The PA problem with the PF objective was considered before only for one NOMA group. When specialized to this case, our algorithm is much faster than in prior work. For the CA subproblem, we prove that it is equivalent to a bipartite graph matching problem, for which efficient solution algorithms exist. We show empirically that the proposed joint PA-CA approach performs very close to exhaustive search for small number of users. Extensive experiments demonstrate that our framework significantly outperforms several benchmark schemes in both system efficiency and fairness.
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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".