Unsupervised ResNet-Inspired Beamforming Design Using Deep Unfolding Technique

Chia Hung Lin, Yen Ting Lee, Wei Ho Chung*, Shih Chun Lin, Ta-Sung Lee

*此作品的通信作者

研究成果: Conference contribution同行評審

9 引文 斯高帕斯(Scopus)

摘要

Beamforming is a key technology in communication systems of the fifth generation and beyond. However, traditional optimization-based algorithms are often computationally prohibited from performing in a real-time manner. On the other hand, the performance of existing deep learning (DL)-based algorithms can be further improved. As an alternative, we propose an unsupervised ResNet-inspired beamforming (RI-BF) algorithm in this paper that inherits the advantages of both pure optimization-based and DL-based beamforming for efficiency. In particular, a deep unfolding technique is introduced to reference the optimization process of the gradient ascent beamforming algorithm for the design of our neural network (NN) architecture. Moreover, the proposed RI-BF has three features. First, unlike the existing DL-based beamforming method, which employs a regularization term for the loss function or an output scaling mechanism to satisfy system power constraints, a novel NN architecture is introduced in RI-BF to generate initial beamforming with a promising performance. Second, inspired by the success of residual neural network (ResNet)-based DL models, a deep unfolding module is constructed to mimic the residual block of the ResNet-based model, further improving the performance of RI-BF based on the initial beamforming. Third, the entire RI-BF is trained in an unsupervised manner; as a result, labelling efforts are unnecessary. The simulation results demonstrate that the performance and computational complexity of our RI-BF improves significantly compared to the existing DL-based and optimization-based algorithms.

原文English
主出版物標題2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁數7
ISBN(電子)9781728182988
DOIs
出版狀態Published - 7 12月 2020
事件2020 IEEE Global Communications Conference, GLOBECOM 2020 - Virtual, Taipei, Taiwan
持續時間: 7 12月 202011 12月 2020

出版系列

名字2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings

Conference

Conference2020 IEEE Global Communications Conference, GLOBECOM 2020
國家/地區Taiwan
城市Virtual, Taipei
期間7/12/2011/12/20

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