ProtoBeam: Generalizing Deep Beam Prediction to Unseen Antennas using Prototypical Networks
Bibliographic record
Abstract
Deep learning (DL) techniques have recently emerged to efficiently manage mmWave beam transmissions without requiring time consuming beam sweeping strategies. A fundamental challenge in these methods is their dependency on hardware-specific training data and their limited ability to generalize. Large drops in performance are reported in literature when DL models trained in one antenna environment are applied in another. This paper proposes the application of Prototypical Networks to address this challenge – and utilizes the DeepBeam real-world dataset [1] to validate the developed solutions. Prototypical Networks (PN) excel in extracting features to establish class-specific prototypes during the training, resulting in precise embeddings that encapsulate the defining features of the data. We demonstrate the effectiveness of PN to enable generalization of deep beam predictors across unseen antennas. Our approach, which integrates data normalization and prototype normalization with the PN, achieves an average beam classification accuracy of 74.11% when trained and tested on different antenna datasets. This is an improvement of 398% compared to baseline performances reported in literature that do not account for such domain shifts. To the best of our knowledge, this work represents the first demonstration of the value of Prototypical Networks for domain adaptation in wireless networks, providing a foundation for future research in this area.
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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 | 0.001 | 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".