Globally Optimal Max-Min Rate Joint Channel and Power Allocation for Hybrid NOMA-OMA Downlink Systems
Bibliographic record
Abstract
This work proposes a globally optimal solution algorithm to the joint power allocation (PA) and channel allocation (CA) problem for downlink hybrid NOMA-OMA systems with the objective of maximizing the minimum user rate. In the hybrid NOMA-OMA scenario, the users are divided into clusters, each cluster shares one channel using NOMA (Non-Orthogonal Multiple Access), while different clusters are assigned channels orthogonally. The optimization problem is converted to the problem of maximizing the user rate under the constraint that all rates be equal. It is further decomposed into PA and CA subproblems, which are solved iteratively. The PA subproblem is handled by first deriving an analytical expression of the total power as a function of the common user rate, and then solving it via bisection search. The CA subproblem keeps the equal-rate assignment fixed and aims to find the CA that minimizes the total power. We prove that the CA subproblem is equivalent to a minimum bipartite graph matching problem, for which efficient algorithms exist. Finally, we demonstrate that the proposed iterative algorithm converges to the globally optimal solution after a finite number of iterations. In addition, we prove that the number of iterations is at most three when the power budget is sufficiently large. Extensive experiments demonstrate the effectiveness of the proposed scheme.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".