RS-Based MIMO-NOMA Systems in Multicast Framework
Bibliographic record
Abstract
This chapter presents a novel scheme that integrates the rate-splitting (RS) technique in Multiple Input Multiple Output (MIMO) systems with non-orthogonal multiple access (NOMA) to improve performance and capacity in wireless communication systems under imperfect channel state information at the transmitter (CSIT) and in overloaded regimes. The proposed approach addresses a general and realistic scenario, incorporating both unicast and multicast users, aiming to increase system throughput through the optimization of precoding vectors and power allocation. A generic power allocation optimization technique is introduced, which can be employed for maximizing both the minimum-rate and sum-rate, focusing on the rate of the weakest user within each group per cluster. To tackle the non-convex nature of the problems, the proposed technique leverages the WMMSE-rate relationship and an alternating optimization (AO) algorithm, transforming the problem into a convex one. The chapter provides a comprehensive analysis of the proposed scheme, offering a tutorial background and presenting novel insights for an enhanced understanding.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".