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Transfer Learning and Meta Learning Based Fast Downlink Beamforming Adaptation
[article]
2020
arXiv
pre-print
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
arXiv:2011.00903v1
fatcat:trt22neppzaljdidr6bzwkul74