Optimization Inspired Learning Network for Multiuser Robust Beamforming
ID:130 View Protection:ATTENDEE Updated Time:2020-08-05 10:17:28 Hits:360 Oral Presentation

Start Time:2020-06-08 14:20(Asia/Shanghai)


Session:S Special Session » SS17Robust Beamforming Based On Convex/Nonconvex Optimization

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For real-time wireless networks with strict latency and energy constraints, deep neural networks have been used to approximate the resource allocation solutions that are previously obtained by advanced but computationally expensive optimization algorithms. In this paper, we consider the multi-user beamforming design problem for sum rate maximization in multi-antenna interference channels. Specifically, we propose a gradient projection inspired recurrent neural network for efficient beamforming optimization. The key ingredient is to explore the structure of the gradient vector of the sum rate so that the network learns only a set of dimension reduced coefficients. Furthermore, we extend it to the robust beamforming design for worst-case sum rate maximization in the presence of bounded channel errors. Numerical results show that the proposed learning networks can achieve high accuracy of the sum rates while with significantly reduced runtime.
Minghe Zhu
The Chinese University of Hong Kong, Shenzhen, China

Submission Author
Minghe Zhu The Chinese University of Hong Kong, Shenzhen, China
Tsung-Hui Chang The Chinese University of Hong Kong, Shenzhen, China
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