On the Design of Channel Coding Autoencoders With Arbitrary Rates for ISI Channels
Bibliographic record
Abstract
This letter presents an autoencoder-based channel coding scheme in the presence of inter-symbol interference (ISI) and additive white Gaussian noise (AWGN), supporting arbitrary coding rates. Both the transmitter and receiver of the proposed autoencoder employ bi-directional gated recurrent unit (Bi-GRU) layers. Additional extra dense layers are applied at the end of the transmitter and at the beginning of the receiver, serving as learnable puncture and depuncture modules, respectively. Different code rates can be achieved by adjusting the output dimension of the extra dense layers. Experimental results demonstrate that the proposed autoencoder significantly outperforms conventional convolutional codes over ISI channels, for multiple code rates. The proposed autoencoder also outperforms LDPC codes in the low signal-to-noise ratio (SNR) regime. The neural codes still require improvement to be competitive in the high SNR regime.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".