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- Title
DCGAN 信道下的端到端通信系统设计.
- Authors
程芳芳; 王旭东; 吴 楠
- Abstract
For the problem of the curse of dimensionality in long-sequence modeling in communication systems, an improved end-to-end communication system design scheme based on Deep Convolutional Generative Adversarial Network ( DCGAN) channel modeling is proposed. The solution combines the Convolutional Neural Network(CNN) with Conditional Generative Adversarial Network(CGAN),and uses the local connection characteristics of CNN and Fully Connected Layer(FC) to model the channel that transmits long sequences. By redesigning the parameters and adjusting the network structure, a learning network that adapts to different modulation methods and channel types is obtained and applied to the end-to- end system where the CNN network is used at the transceiver end as a bridge for gradient back propagation between the transmitter and the receiver. Simulation experiments show that the improved DCGAN network can successfully implement long-sequence modeling with a reduced network scale and computational complexity, and has shown good generalization capabilities. In addition, by applying the modeling results to the end-to-end communication system design, a bit error rate performance similar to that of traditional digital modulation system can be achieved.
- Subjects
GENERATIVE adversarial networks; BACK propagation; TELECOMMUNICATION systems; CONVOLUTIONAL neural networks; DIGITAL modulation; DIGITAL communications
- Publication
Telecommunication Engineering, 2022, Vol 62, Issue 6, p742
- ISSN
1001-893X
- Publication type
Article
- DOI
10.3969/j.issn.1001-893x.2022.06.008