Nonlinearity-Aware End-to-End Learning Architecture for Next Generation Wireless Backhaul
Bibliographic record
Abstract
This study presents an End-to-End Learning (ETEL) architecture designed for wireless backhaul links, tackling hardware-induced impairments such as Power Amplifier Non-Linearity (PA NL) and phase noise. Leveraging geometric constellation shaping, a fully connected neural network (NN) at the transmitter, and a residual-network-based convolutional neural network at the receiver, our solution effectively handles challenges posed by higher-order modulation and diverse NLs. Through joint transceiver NN training with a weighted loss function accounting for throughput, PA linearization, and spectrum mask requirements, our architecture outperforms conventional schemes in strong NL scenarios, supporting constellation sizes up to 256. This positions the proposed ETEL solution as a promising candidate for future backhaul links operating at mmW or sub-THz bands envisioned for 6G networks.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".