One-Bit Precoding Constellation Design via Autoencoder-Based Deep Learning
Bibliographic record
Abstract
This paper considers a multicasting system in which the base station has a large number of antennas with cost-effective one-bit digital-to-analog converters and aims to send a common symbol to multiple remote users. Unlike the existing literature which seeks to design the one-bit precoder for a given constellation, e.g., quadrature amplitude modulation (QAM) or phase shift keying (PSK), this paper aims to jointly design the transmit one-bit precoder and the receive constellation by leveraging the concept of autoencoder, wherein the end-to-end multicasting system is modeled using a deep neural network with the one-bit precoding constraint represented by a binary thresholding layer. To deal with the issue that such a binary layer always produces a gradient of zero, and thus prevents an effective end-to-end training when using the conventional back-propagation method, this paper uses a variant of straight-through estimator which approximates the thresholding function with a properly scaled sigmoid function in the back-propagation phase. Numerical results show that, for a fixed channel scenario, the proposed autoencoder-based constellation design is superior to the conventional QAM and PSK constellations. Using the insights obtained from fixed channel scenarios, we also propose a constellation design for varying channel scenarios and numerically show that the proposed design achieves a better performance as compared to the conventional 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".