Reference : Transfer Learning and Meta Learning Based Fast Downlink Beamforming Adaptation
Scientific journals : Article
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/45600
Transfer Learning and Meta Learning Based Fast Downlink Beamforming Adaptation
English
Yuan, Yi []
Zheng, G. [> >]
Wong, K.-K. [> >]
Ottersten, Björn mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) >]
Luo, Z.-Q. [> >]
16-Nov-2020
IEEE Transactions on Wireless Communications
1-1
Yes
[en] Array signal processing;Wireless communication;Training;Adaptation models;Task analysis;Resource management;Uplink;Deep transfer learning;meta-learning;online learning;beamforming;MISO;SINR balancing
[en] 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.
http://hdl.handle.net/10993/45600
10.1109/TWC.2020.3035843

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
09257198_3_Transfer Learning.pdfPublisher postprint2.28 MBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.