Transfer Learning and Meta Learning Based Fast Downlink Beamforming Adaptation

TL;DR

This paper introduces transfer and meta-learning algorithms for rapid downlink beamforming adaptation in wireless systems, addressing environment changes and outperforming traditional deep learning methods.

Contribution

It develops offline transfer and meta-learning algorithms, along with an online meta-learning approach, to improve beamforming adaptation in dynamic wireless environments.

Findings

Meta-learning outperforms transfer learning in adaptation speed and accuracy.

Online meta-learning surpasses offline methods in non-stationary environments.

Proposed algorithms significantly outperform non-adaptive deep learning approaches.

Abstract

This paper studies fast adaptive beamforming optimization for the signal-to-interference-plus-noise ratio balancing problem in a multiuser multiple-input single-output downlink system. Existing deep learning based approaches to predict beamforming rely on the assumption that the training and testing channels follow the same distribution which may not hold in practice. As a result, a trained model may lead to performance deterioration when the testing network environment changes. To deal with this task mismatch issue, we propose two offline adaptive algorithms based on deep transfer learning and meta-learning, which are able to achieve fast adaptation with the limited new labelled data when the testing wireless environment changes. Furthermore, we propose an online algorithm to enhance the adaptation capability of the offline meta algorithm in realistic non-stationary environments. Simulation results demonstrate that the proposed adaptive algorithms achieve much better performance than the direct deep learning algorithm without adaptation in new environments. The meta-learning algorithm outperforms the deep transfer learning algorithm and achieves near optimal performance. In addition, compared to the offline meta-learning algorithm, the proposed online meta-learning algorithm shows superior adaption performance in changing environments.