Supervised Learning for Nulling Angle Prediction with Measured Self-Interference Channels for Full-Duplex Isolation Enhancement
Bibliographic record
Abstract
This paper introduces a neural network (NN)-based nulling control beamforming scheme that leverages measured self-interference (SI) channel data to minimize mutual coupling (MC) in full-duplex large-scale antenna arrays. Two NN models using an optimized nulling angle dataset generated by Particle Swarm Optimization algorithms are trained, one model incorporates convolutional layers, while the other consists solely of fully connected layers. The results demonstrate that both models achieve significant performance improvements, with an average beam-level MC reduction of 9.3 dB, resulting in an average Tx-Rx isolation exceeding 74.7 dB. Specifically, over 92.6% of downlink and uplink user locations exhibit enhanced beam-level isolation, and 46.5% of test cases achieve isolation improvements greater than 10 dB. Additionally, the training time efficiency and accuracy between the two NN models are investigated.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".