Joint Design of User Clustering, Beamforming, and Power Allocation for NOMA
Bibliographic record
Abstract
This paper investigates the application of downlink beamforming along with non-orthogonal multiple access (NOMA) in a multiple-input multiple-output (MIMO) system. The joint design of user clustering, downlink beamforming and power allocation is formulated as a mixed-integer non-linear programming (MINLP) model, aiming to minimize the total transmission power while satisfying quality-of-service (QoS) and power constraints. To tackle this challenging problem, we reformulate it into a more tractable form and conceive a low complexity algorithm based on the penalty dual decomposition technique for its solution. The performance of the proposed NOMA scheme with joint design of user clustering, beamforming and power allocation is validated by means of simulations over millimeter-wave (mmWave) channels. The results show the advantages of the proposed scheme in terms of total transmit power and spectral efficiency over competing multiple access schemes.
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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".