An End-to-End Auto-Encoder Algorithm for Hardware-Impaired Transceivers Based on Meta and Joint Learning
Bibliographic record
Abstract
The application of Deep learning (DL) in wireless communications has achieved remarkable success. However, there are still marked challenges impeding its use in the physical layer, largely due to the random nature of the wireless channel. This work investigates an end-to-end transceiver based on an unsupervised auto-encoder. We aim to detect a wireless signal suffering from transceiver hardware impairments as it passes through the fading channel. Furthermore, we examine the ability of this receiver to detect signals corrupted with non-Gaussian noise. We use meta and joint learning techniques to train the auto-encoder transceiver with random unknown channels and HWIs to simulate real channel conditions. We compare the results with maximum likelihood detection (MLD) in terms of bit error rate (BER). Our results demonstrate that using meta-learning to train the auto-encoder enhances system performance by reducing the BER of the received signal.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".