Modulation-Specific Multiuser Transmit Precoding and User Selection for One-Dimensional Signaling
Bibliographic record
Abstract
Massive deployment of low data rate Internet of things and eHealth devices that require high reliability motivates the development of practical precoding and user selection techniques. In this paper, we show that throughput and communications reliability can be improved by incorporating knowledge of modulation type in the design of the multiuser transmit precoder. The transmission of low data rate one-dimensionally modulated signals in a broadcast channel is considered. The transmit precoding matrix is determined by minimizing the weighted sum of error probabilities of users. Although the proposed minimum probability of error (MPE) precoding problem is nonconvex and highly nonlinear, it is solved by the alternating minimization of two convex subproblems. A reduced-complexity version of convex MPE precoding is then introduced, which exponentially reduces the complexity of the problem. Numerical results show that the proposed precoding techniques significantly improve system performance in broadcast channels. A user selection algorithm, compatible with MPE precoding, is also proposed that selects users if their error probabilities can approach zero. Based on line packing principles in Grassmannian manifolds, it is shown that the number of selected users could potentially be more than the number of transmit antennas, which translates to supporting more simultaneous users in the shared channel compared to existing user selection methods.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 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".