Domain Adaptation in Beam Management using Asymmetric Autoencoder and Transfer Learning
Bibliographic record
Abstract
In 5G millimeter wave (mmWave) networks, beam management is crucial for establishing optimal transmitter-receiver beam pairs between the base station (gNB) and user equipment (UE) through sequential adjustment of azimuth and elevation angles and transmit power. Conventional approaches like exhaustive search increase exponentially with growing beam configurations, while predefined codebook-based beamforming vectors yield suboptimal solutions in dynamic networks. Although machine learning (ML) techniques offer potential solutions by learning efficient beam selection policies, the high-dimensional search space makes direct policy learning computationally intensive. Transfer learning (TL) can leverage knowledge from simpler network optimization tasks; however, dimensional mismatches between source and target domains present significant challenges. To address these dimensional mismatches, we present a novel domain adaptation (DA) framework that uses an asymmetric autoencoder combined with Maximum Mean Discrepancy (MMD) loss to align latent representations of source and target domains. This architecture achieves state space reconstruction accuracy exceeding 93% and latent space alignment with total loss below 0.01. Building on this framework, we introduce two transfer learning methods for beam management: a representation transfer learning with fine-tuning (RTLF) approach leveraging the pretrained encoder, and an adaptive source weight transfer (ASWTR) mechanism combining the encoder with selective source domain weight transfer. Compared to baseline training with DRL, both TL methods demonstrate minimum 16% faster convergence and 81% reduction in training episode steps. The proposed TL methods demonstrate comparable beam management performance to baseline DRL in throughput and energy efficiency, while maintaining optimal beam configurations and outperforming the non-DRL method by 50% in energy efficiency and 45% in throughput with 20 UEs.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".