Enabling High-Order Modulation Over Fading Channels Using E2E Deep Learning-Based Transceiver Optimization
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Bibliographic record
Abstract
In modern wireless communication systems, using high-order modulation is often key to achieving high data rates while maintaining a good spectral efficiency. By packing more bits into each transmitted symbol, more data can be sent over a given bandwidth, but that also makes the transmission more vulnerable to fading and noise. Moreover, traditional methods often isolate the design and optimization of transmitters and receivers, leading to poor error rate performances. To tackle these challenges, we propose a novel end-to-end (E2E) optimization strategy that includes a trainable constellation at the transmitter and a convolutional neural network (CNN)based demapper at the receiver. The transmitter and receiver are jointly trained to handle channel fading and detect symbols with minimal errors. Our findings demonstrate that the proposed transceiver architecture exhibits a significantly lower bit-error rate (BER) than traditional modulation methods and earlier dense layer-based models, especially for high-order modulation.
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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 | 0.000 |
| 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 it