Domain Adaptation in Beam Management using Asymmetric Autoencoder and Transfer Learning

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.