  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.
