Geometric Constellation Shaping Using Initialized Autoencoders
Why this work is in the frame
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Bibliographic record
Abstract
Geometric constellation shaping is a promising technique to boost the transmission capacity of communication systems. Earlier, traditional optimization methods in constellation design lead to several advanced quadrature amplitude modulation (QAM) formats, such as star QAM, cross QAM, and hexagonal QAM. The difficulty in determining decision boundaries limited their use in real systems. To overcome this, machine learning based geometric constellation shaping has recently been proposed, where the detection is done via neural networks. Unfortunately, the resulting constellation shape is often unstable and highly dependent on initialization. In this paper, we use an autoencoder for constellation shaping and detection, with strategic initialization. We contrast initialization with hexagonal QAM and square QAM. We present numerical results showing the hexagonal QAM initialization achieves the best symbol error rate performance, while the square QAM initialization has better bit error rate performance.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it