Efficient MIMO-NOMA Clustering Integrating Joint Beamforming and Power Allocation
Bibliographic record
Abstract
Multiple-input, multiple-output non-orthogonal multiple access (MIMO-NOMA) approach has been considered as a promising multiple access technology for the fifth generation (5G) networks to improve the system capacity and the spectral efficiency. In this paper, we propose a linear optimization approach to jointly optimize beamforming vectors and power allocation coefficients for a MIMO- NOMA cluster, and then minimize the total power consumption through mobile user (MU) clustering. The proposed approach avoids the peer effect during MU clustering and can obtain a closed-form expression of the cluster beamforming matrix. Then, we employ our approach in two different MIMO-NOMA scenarios termed as MIMO- NOMA1 and MIMO-NOMA2. Based on the analytical results, we propose a ranking scheme to obtain the clustering result for the large-scale MIMO-NOMA networks. Simulation results show that MIMO-NOMA1 is better than MIMO-NOMA2, and the proposed approach is superior in finding power efficient MIMO-NOMA clusters than counterparts.
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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".