Design of High-SNR Multidimensional Constellations for Orthogonal Transmission in a Nakagami- ${m}$ Fading Channel
Bibliographic record
Abstract
Finding the best signal constellation for different communication channels is one of the fundamental problems in digital communication. This problem has been studied widely from different angles and many methods have been proposed for designing good practical signal constellations. There has been a rejuvenated interest in designing good constellations during last decade, in part due to the advent of novel optimization techniques. Nevertheless, most of the recent work, similar to the older work in this area, aims to optimize the constellation within a presumed structure (such as points lying on concentric rings). In this paper, we develop a different approach: we aim to optimize constellations based on a Chernoff bound on the probability of error in the versatile Nakagami-m fading channel. We derive two general bounds on the symbol error rate and bit error rate performance of orthogonal transmission in a Nakagami-m fading channel for single-input single-output and orthogonal space-time block codes and we show that a substantial improvement in the error probability is achieved with the novel constellations that are optimized using these bounds.
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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".