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Deep Learning Methods for Universal MISO Beamforming
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- Authors
- Junbeom Kim, Hoon Lee, Seung-Eun Hong, Seok-Hwan Park
- Issue Date
- 2020-11
- Citation
- IEEE Wireless Communications Letters, v.9, no.11, pp.1894-1898
- ISSN
- 2162-2337
- Publisher
- IEEE
- Language
- English
- Type
- Journal Article
- Abstract
- This letter studies deep learning (DL) approaches to optimize beamforming vectors in downlink multi-user multi-antenna systems that can be universally applied to arbitrarily given transmit power limitation at a base station. We exploit the sum power budget as side information so that deep neural networks (DNNs) can effectively learn the impact of the power constraint in the beamforming optimization. Consequently, a single training process is sufficient for the proposed universal DL approach, whereas conventional methods need to train multiple DNNs for all possible power budget levels. Numerical results demonstrate the effectiveness of the proposed DL methods over existing schemes.
- KSP Keywords
- Beamforming optimization, Conventional methods, Deep neural network(DNN), Existing schemes, Learning methods, Multi-antenna systems, Numerical results, Power Constraint, Power Limitation, Transmit Power, base station