GWO-Based User Clustering And Power Allocation For Downlink MIMO-NOMA Systems
Bibliographic record
Abstract
Due to the rapid technological progress and the growing demand for massive connectivity, cutting-edge technologies, including massive Multi-Input Multi-Output (mMIMO) and Non-Orthogonal Multiple Access (NOMA), are essential to optimize beyond 5G networks. Efficient resource allocation strategies, such as user clustering and power allocation techniques, considerably improve the performance of downlink MIMO-NOMA wireless communication systems. Despite their notable advantages, these methods lead to computational complexity challenges driven by multiple variables and constraints. Therefore, we introduce a framework based on Grey Wolf Optimizer (GWO) to perform power allocation and user clustering. The adaptability and efficiency of this meta-heuristic optimization approach enable our method to handle the massive access scenario by maximizing the system capacity and improving energy efficiency (EE) and spectral efficiency (SE). Simulation results demonstrate the superiority of this GWO-based approach, exhibiting significant improvements in the number of served users and a decrease in the transmit power compared to conventional resource allocation techniques. This study marks a significant step forward in optimizing the MIMO-NOMA system. It sets the stage for further advancements in resource allocation strategies and potentially revolutionizes 5 G and beyond wireless communications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".