Deep Learning-Based Transceiver Design for Additive Non-Gaussian Impulsive Noise Channels
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Bibliographic record
Abstract
This paper presents a deep-learning (DL)-based transceiver design on additive non-Gaussian impulsive noise (IN) channels. At first, an impulsive generative adversarial network (IGAN) with specific regularization terms is proposed to capture the IN behaviours and used as the channel noise simulator (CNS). Subsequently, the transmitter and receiver are jointly optimized to develop both optimal transmit signal and detection. Furthermore, to enhance the detection performance, the multi-level wavelet signal recovery network (MWSRN) is applied to construct a preprocessor to combat both IN and channel fading without pilots. Illustrative simulation results show that the proposed scheme can achieve better convergence and bit-error rate (BER) performance under different IN settings than various existing approaches.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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