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11 November 2021 Residual neural network based joint transmit-receive antenna selection in MIMO system
Zhibin Guo, Jiaxin Cai
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Proceedings Volume 12076, 2021 International Conference on Image, Video Processing, and Artificial Intelligence; 120760X (2021) https://doi.org/10.1117/12.2620214
Event: Fourth International Conference on Image, Video Processing, and Artificial Intelligence (IVPAI 2021), 2021, Shanghai, China
Abstract
In large-scale MIMO system, as the number of antennas increases, the huge computational complexity makes traditional antenna selection algorithms impossible to effectively apply. This paper propose a joint transmitreceive antenna selection model based on ResNet. We utilize the optimal antenna selection algorithm to create labels for all channel matrices, which based on maximizing channel capacity criterion. Then using large-scale channel data to train a powerful residual neural network classifier. Consequently the trained model can classify the corresponding label for each channel matrix in the test set and select the optimal antenna subset. Experimental results show that the method can effectively decrease the number of antenna selection and its communication performance outperforms compared methods
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Zhibin Guo and Jiaxin Cai "Residual neural network based joint transmit-receive antenna selection in MIMO system", Proc. SPIE 12076, 2021 International Conference on Image, Video Processing, and Artificial Intelligence, 120760X (11 November 2021); https://doi.org/10.1117/12.2620214
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KEYWORDS
Antennas
Neural networks
Signal to noise ratio
Telecommunications
Classification systems
Wireless communications
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