•  
  •  
 

Journal of System Simulation

Abstract

Abstract: In view of the over-fitting problem that caused by multiple parameters and high memory usage of the full connection layer of neural network in training, a RIS-based element grouping areal fully connected neural network (RGFCNN) is proposed for the first time based on the structural characteristics of reconfigurable intelligence surface (RIS). Based on the structural characteristics of RIS, the network is optimized on traditional FCNN. A novel transmission surface attention mechanism is designed for the effective feature extraction of data. Compared with the traditional FCNNs, the proposed network does not arrange the data in one-dimensional manner. Instead, a element grouping strategy is proposed for the neural network construction, which directly groups the two-dimensional surface data, carries out the fully connected processing on each group, and concatenates the output of each group. The experimental results show that, on the public available communication signal datasets with IQ data features, RGFCNN has better recognition accuracy when SNR is greater than 0 dB, and the training parameters are approximately 1/6 of the original.

First Page

1017

Last Page

1027

CLC

TP183

Recommended Citation

Hou Shunhu, Fang Shengliang, Zeng Qingyao, et al. Element Grouping Faceted Fully Connected Network Based on RIS[J]. Journal of System Simulation, 2024, 36(4): 1017-1027.

Corresponding Author

Fang Shengliang

DOI

10.16182/j.issn1004731x.joss.23-0304

Download

Share

COinS