A Learning Approach to Cooperative Communication System Design

The cooperative relay network is a type of multi-terminal communication system. We present in this paper a Neural Network (NN)-based autoencoder (AE) approach to optimize its design. This approach implements a classical three-node cooperative system as one AE model, and uses a two-stage scheme to tr...

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Published in:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 5240 - 5244
Main Authors: Lu, Yuxin, Cheng, Peng, Chen, Zhuo, Mow, Wai Ho, Li, Yonghui
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2020
Subjects:
Artificial neural networks
Autoen-coder
Communication systems
Cooperative systems
Neural network
Relay network
Robustness
Signal to noise ratio
Testing
Training
ISSN:2379-190X
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Abstract The cooperative relay network is a type of multi-terminal communication system. We present in this paper a Neural Network (NN)-based autoencoder (AE) approach to optimize its design. This approach implements a classical three-node cooperative system as one AE model, and uses a two-stage scheme to train this model and minimize the designed losses. We demonstrate that this approach shows performance close to the best baseline in decode-and-forward (DF), and outperforms the best baseline in amplify-and-forward (AF), over a wide range of signal-to-noise-ratio (SNR) values. It is also shown that training at a list of mixed SNR values can improve the error performance compared to training at a fixed SNR value. Moreover, to verify the robustness of the trained AE model, we test it under the effect of impulse-noise.
AbstractList The cooperative relay network is a type of multi-terminal communication system. We present in this paper a Neural Network (NN)-based autoencoder (AE) approach to optimize its design. This approach implements a classical three-node cooperative system as one AE model, and uses a two-stage scheme to train this model and minimize the designed losses. We demonstrate that this approach shows performance close to the best baseline in decode-and-forward (DF), and outperforms the best baseline in amplify-and-forward (AF), over a wide range of signal-to-noise-ratio (SNR) values. It is also shown that training at a list of mixed SNR values can improve the error performance compared to training at a fixed SNR value. Moreover, to verify the robustness of the trained AE model, we test it under the effect of impulse-noise.
Author Lu, Yuxin
Mow, Wai Ho
Cheng, Peng
Li, Yonghui
Chen, Zhuo
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Snippet The cooperative relay network is a type of multi-terminal communication system. We present in this paper a Neural Network (NN)-based autoencoder (AE) approach...
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SubjectTerms Artificial neural networks
Autoen-coder
Communication systems
Cooperative systems
Neural network
Relay network
Robustness
Signal to noise ratio
Testing
Training
Title A Learning Approach to Cooperative Communication System Design
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