Attention-Based Deep Learning for Hybrid Beamforming in OFDM Systems With Phase Noise
Bibliographic record
Abstract
We introduce a deep learning-based hybrid beamforming (HBF) strategy for millimeter-wave transmission systems, specifically addressing the challenges posed by phase noise of local oscillators. Our approach utilizes a deep neural network to optimize precoding and combining matrices based on channel state information. We incorporate the symbol index through an adaptive attention mechanism and employ a self-supervised learning approach with a phase-noise-aware loss function to mitigate the effects of phase noise. While primarily focused on phase noise, our method also accommodates other practical constraints, such as limited-resolution phase shifter and imperfect channel estimation. Simulation results demonstrate that our design outperforms traditional and deep-learning based HBF methods in terms of data rate both in scenarios impacted only by phase noise and compounded distortion scenarios including low-resolution phase shifters and channel estimation errors.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".