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Journal Article
Implementation Methodologies of Deep Learning-Based Signal Detection for Conventional MIMO Transmitters
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- Issue Date
- 2019-09
- Citation
- IEEE Transactions on Broadcasting, v.65, no.3, pp.636-642
- ISSN
- 0018-9316
- Publisher
- IEEE
- Language
- English
- Type
- Journal Article
- Abstract
- In this paper, simple methodologies of deep learning application to conventional multiple-input multiple-output (MIMO) communication systems are presented. The deep learning technologies with deep neural network (DNN) structure, emerging technologies in various engineering areas, have been actively investigated in the field of communication engineering as well. In the physical layer of conventional communication systems, there are practical challenges of application of DNN: calculating complex number in DNN and designing proper DNN structure for a specific communication system model. This paper proposes and verifies simple solutions for the difficulty. First, we apply a basic DNN structure for signal detection of one-tap MIMO channel. Second, convolutional neural network (CNN) and recurrent neural network (RNN) structures are presented for MIMO system with multipath fading channel. Our DNN structure for one-tap MIMO channel can achieve the optimal maximum likelihood detection performance, and furthermore, our CNN and RNN structures for multipath fading channel can detect the transmitted signal properly.
- KSP Keywords
- Communication system, Complex Number, Convolution neural network(CNN), Deep learning application, Deep neural network(DNN), Emerging technology, Learning Technology, Learning-based, MIMO channel, MIMO system, MIMO transmitters