8-QAM Division for Uplink Massive SIMO Systems
Bibliographic record
Abstract
In this letter, we consider an ultra-reliable low-latency communication (URLLC) scheme for one uplink massive single-input multiple-output (SIMO) system with three users. In our design, to enable all the users to update status information to the base station (BS) concurrently with an extremely low latency, we specifically decompose a cross 8-QAM constellation into one additive uniquely decomposable constellation group (AUDCG), and then devise a new multiuser space-time modulation scheme at the transmitters accordingly. For the receiver side, to assure that the BS can detect all the transmitted signals of different users simultaneously with a high reliability and a low latency, we investigate a noncoherent maximum likelihood (ML) receiver in comparison to a minimum Riemannian distance (MRD) estimator and a minimum Euclidean distance (MED) detector when the antenna array size is scaled up. Finally, extensive computer simulations are carried out to validate the effectiveness of our proposed design when the antenna array size is large.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".