An Improved Coalition Game Approach for MIMO-NOMA Clustering Integrating Beamforming and Power Allocation
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Bibliographic record
Abstract
The multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) 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 cluster beamforming strategy to jointly optimize beamforming vectors and power allocation coefficients for mobile users (MUs) in MIMO-NOMA clustering with the aim of reducing the total power consumption. This approach avoids the peer effect during the process of beamforming matrix calculation and can obtain a closed-form solution of beamforming strategy for multi-MU MIMO-NOMA clusters. To minimize the total power consumption, we further propose an improved coalition game approach to effectively optimize MU clustering for the large-scale MIMO networks, in which the size of a cluster is flexible. Furthermore, we discuss two different MIMO-NOMA scenarios and show that employing a NOMA power coefficient set can achieve a better performance than employing a single NOMA power coefficient for each MU in a cluster. Simulation results include the performance analysis for a single MIMO-NOMA cluster and the clustering result for a large-scale MIMO system with many MUs, which show that the proposed approach is superior than counterparts in finding the power efficient MIMO-NOMA clusters.
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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 it