Spectral- and Energy-Efficient Resource Allocation for Multi-Carrier Uplink NOMA Systems
Bibliographic record
Abstract
In this paper, resource allocation for a multi-carrier uplink non-orthogonal multiple access (NOMA) system is studied. Unlike the existing works on multi-carrier uplink NOMA, in which each user is assumed to access only one subcarrier, we consider a more general scenario where the number of subcarriers allocated to a single user is not constrained. We first aim to maximize the system's sum rate, which requires selecting the appropriate subcarriers for each user and distribute the transmission power. The formulated non-convex problem is transformed into a convex one, and further, an optimal and low-complexity iterative water-filling solution is proposed. Nonetheless, it is shown that maximum transmit power is employed by each user to maximize the sum rate. Motivated by the fact that the users are power constrained, the energy efficiency (EE) maximization problem is also studied. Based on fractional programming, the EE maximization problem is transformed into a series of sum rate maximization subproblems, and the proposed iterative water-filling solution is applied to each subproblem. The proposed schemes are compared with other NOMA-based and orthogonal multiple access based algorithms, and its superiority is fully validated.
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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 | 0.001 |
| 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) | 0.001 | 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".