Multi-User Detection Using ADMM-Based Compressive Sensing for Uplink Grant-Free NOMA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Non-orthogonal multiple access (NOMA) is considered a primary candidate addressing the challenge of massive connectivity in fifth generation wireless communication systems. In this letter, we propose a low-complexity NOMA mechanism with efficient multi-user detection (MUD) based on the adaptive alternating direction method of multipliers, which is able to jointly detect user activity and transmitted data. The proposed algorithm leverages the transmit symbol estimate and active user set as “prior knowledge,” which can be obtained from the previous iterations/time intervals, for improved MUD performance. We demonstrate that our proposed mechanism outperforms the state-of-art MUD NOMA schemes.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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