RS-Based MIMO-NOMA Systems in Multicast Framework
Why this work is in the frame
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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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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it