Related papers: Deep Unfolded Multicast Beamforming

Deep Unfolded Multicast Beamforming

URL: http://arxiv.org/abs/2004.09345v1
Date: Mon, 20 Apr 2020 14:44:43 GMT
Title: Deep Unfolded Multicast Beamforming
Authors: Satoshi Takabe and Tadashi Wadayama
Abstract summary: Multicast beamforming is a promising technique for multicast communication. Deep learning-based approaches have been proposed for beamforming design. In this paper, we propose a novel deep unfolded trainable beamforming design with high scalability and efficiency.
Score: 20.50873301895484
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Multicast beamforming is a promising technique for multicast communication. Providing an efficient and powerful beamforming design algorithm is a crucial issue because multicast beamforming problems such as a max-min-fair problem are NP-hard in general. Recently, deep learning-based approaches have been proposed for beamforming design. Although these approaches using deep neural networks exhibit reasonable performance gain compared with conventional optimization-based algorithms, their scalability is an emerging problem for large systems in which beamforming design becomes a more demanding task. In this paper, we propose a novel deep unfolded trainable beamforming design with high scalability and efficiency. The algorithm is designed by expanding the recursive structure of an existing algorithm based on projections onto convex sets and embedding a constant number of trainable parameters to the expanded network, which leads to a scalable and stable training process. Numerical results show that the proposed algorithm can accelerate its convergence speed by using unsupervised learning, which is a challenging training process for deep unfolding.

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