Energy-Efficient Uniquely Factorable Constellation Designs for Noncoherent SIMO Channels
Bibliographic record
Abstract
In this paper, a novel concept called a uniquely factorable constellation (UFC) is proposed for the systematic design of noncoherent full-diversity constellations for a wireless communication system that is equipped with a single transmitter antenna and multiple receiver antennas [single input-multiple output (SIMO)], where neither the transmitter nor the receiver knows channel-state information. It is proved that such a UFC design guarantees the unique blind identification of channel coefficients and transmitted signals in a noise-free case by processing only two received signals, as well as full diversity for the noncoherent maximum-likelihood (ML) receiver in a noise case. By using the Lagrange four-square theorem, an algorithm is developed to efficiently and effectively design various sizes of energy-efficient unitary UFCs to optimize the coding gain. In addition, a closed-form optimal energy scale is found to maximize the coding gain for the unitary training scheme based on the commonly used quadratic-amplitude modulation (QAM) constellations. Comprehensive computer simulations show that, using the same generalized likelihood ratio test (GLRT) receiver, the error performance of the unitary UFC designed in this paper outperforms the differential scheme, the optimal unitary training scheme presented in this paper, and the signal-to-noise ratio (SNR) efficient training scheme using the QAM constellation, which, so far, yields the best error performance in the current literature. However, with the same training ML receiver, the error performance of the UFCs is worse than the training scheme based on the QAM constellations.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".