An Improved Coalition Game Approach for MIMO-NOMA Clustering Integrating Beamforming and Power Allocation
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".