Joint Transmission Scheduling and Power Allocation in Non-Orthogonal Multiple Access
Bibliographic record
Abstract
Multi-carrier based non-orthogonal multiple access (NOMA) is an effective method to meet the ever-increasing demands of both user throughput and energy efficiency by multiplexing multiple users on the same carrier. Since interference from users with a poorer channel gain can be canceled at a user with a strong channel gain by successive interference cancellation, NOMA can enhance the system performance. To improve the downlink system performance, it is crucial to appropriately determine users scheduled on each carrier and power allocation at the base station. However, the existing works are generally either heuristic or local optimal due to the mixed optimization problem. In this paper, we focus on the global optimal solutions to maximize user throughput and energy efficiency in NOMA, respectively. In particular, we first formulate the mixed integer optimization problem which are intractable to be solved. Fortunately, by the provided analytical results, the optimization models can be largely simplified. Then, we propose the architectures of joint user scheduling and power allocation in NOMA, as well as the corresponding optimal algorithms. Simulation results demonstrate that our proposed algorithms indeed outperform existing works in terms of the user throughput and energy efficiency, respectively.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".