Element Grouping Faceted Fully Connected Network Based on RIS

doi:10.16182/j.issn1004731x.joss.23-0304

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.

Key words: reconfigurable intelligence surface (RIS), fully-connected neural network(FCNN), element grouping strategy, IQ signal, modulation recognition

CLC Number:

TP183

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

Figures/Tables 13

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References 26

1	赵亚军, 郁光辉, 徐汉青. 6G移动通信网络:愿景、挑战与关键技术[J]. 中国科学(信息科学), 2019, 49(8): 963-987.
	Zhao Yajun, Yu Guanghui, Xu Hanqing. 6G Mobile Communication Networks: Vision, Challenges, and Key Technologies[J]. Scientia Sinica(Informationis), 2019, 49(8): 963-987.
2	Tariq F, Khandaker M R A, Wong K K, et al. A Speculative Study on 6G[J]. IEEE Wireless Communications, 2020, 27(4): 118-125.
3	高子路, 孙韶辉, 李丽. 面向新一代移动通信的智能超表面技术综述[J]. 电信科学, 2022, 38(10): 20-35.
	Gao Zilu, Sun Shaohui, Li Li. Overview of Reconfigurable Intelligent Surface for New-generation Mobile Communication[J]. Telecommunications Science, 2022, 38(10): 20-35.
4	张磊, 崔铁军. 时空编码数字超材料和超表面研究进展[J]. 中国科学基金, 2021, 35(5): 694-700.
	Zhang Lei, Cui Tiejun. Recent Progress of Space-time-coding Digital Metamaterials and Metasurfaces[J]. Bulletin of National Natural Science Foundation of China, 2021, 35(5): 694-700.
5	张磊, 陈晓晴, 郑熠宁, 等. 电磁超表面与信息超表面[J]. 电波科学学报, 2021, 36(6): 817-828.
	Zhang Lei, Chen Xiaoqing, Zheng Yining, et al. Electromagnetic Metasurfaces and Information Metasurfaces[J]. Chinese Journal of Radio Science, 2021, 36(6): 817-828.
6	Yu Nanfang, Genevet P, Kats M A, et al. Light Propagation with Phase Discontinuities: Generalized Laws of Reflection and Refraction[J]. Science, 2011, 334(6054): 333-337.
7	Cui Tiejun, Qi Meiqing, Wan Xiang, et al. Coding Metamaterials, Digital Metamaterials and Programmable Metamaterials[J]. Light: Science & Applications, 2014, 3(10): e218.
8	Mehmet Ali Aygül, Nazzal Mahmoud, Arslan Hüseyin. Deep Learning-based Optimal RIS Interaction Exploiting Previously Sampled Channel Correlations[C]//2021 IEEE Wireless Communications and Networking Conference (WCNC). Piscataway, NJ, USA: IEEE, 2021: 1-6.
9	Xu Meng, Zhang Shun, Ma Jianpeng, et al. Deep Learning-based Time-varying Channel Estimation for RIS Assisted Communication[J]. IEEE Communications Letters, 2022, 26(1): 94-98.
10	朱正. 基于深度学习的太赫兹超表面设计方法研究[D]. 成都: 电子科技大学, 2021.
	Zhu Zheng. Terahertz Metasurface Design Method Based on Deep Learning[D]. Chengdu: University of Electronic Science and Technology of China, 2021.
11	安建成. 面向可重构智能表面的信道估计与被动波束赋形技术研究[D]. 成都: 电子科技大学, 2021.
	An Jiancheng. Channel Estimation and Passive Beamforming for Reconfigurable Intelligent Surface-assisted Communications[D]. Chengdu: University of Electronic Science and Technology of China, 2021.
12	Tang Wankai, Chen Mingzheng, Chen Xiangyu, et al. Wireless Communications with Reconfigurable Intelligent Surface: Path Loss Modeling and Experimental Measurement[J]. IEEE Transactions on Wireless Communications, 2021, 20(1): 421-439.
13	Zhang Lei, Chen Xiaoqing, Liu Shuo, et al. Space-time-coding Digital Metasurfaces[J]. Nature Communications, 2018, 9(1): 4334.
14	Cui Tiejun, Li Lianlin, Liu Shuo, et al. Information Metamaterial Systems[J]. iScience, 2020, 23(8): 101403.
15	Yang Yifei, Zheng Beixiong, Zhang Shuowen, et al. Intelligent Reflecting Surface Meets OFDM: Protocol Design and Rate Maximization[J]. IEEE Transactions on Communications, 2020, 68(7): 4522-4535.
16	Elbir Ahmet M, Mishra K V. A Survey of Deep Learning Architectures for Intelligent Reflecting Surfaces[EB/OL]. (2022-07-21) [2023-01-22]. .
17	Zhang Shunbo, Zhang Shun, Gao Feifei, et al. Deep Learning Optimized Sparse Antenna Activation for Reconfigurable Intelligent Surface Assisted Communication[J]. IEEE Transactions on Communications, 2021, 69(10): 6691-6705.
18	Huang Chongwen, Mo Ronghong, Yuen Chau. Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning[J]. IEEE Journal on Selected Areas in Communications, 2020, 38(8): 1839-1850.
19	Feng Keming, Wang Qisheng, Li Xiao, et al. Deep Reinforcement Learning Based Intelligent Reflecting Surface Optimization for MISO Communication Systems[J]. IEEE Wireless Communications Letters, 2020, 9(5): 745-749.
20	Zuo Chao, Qian Jiaming, Feng Shijie, et al. Deep Learning in Optical Metrology: A Review[J]. Light: Science & Applications, 2022, 11(1): 39.
21	Choudhary K, DeCost B, Chen Chi, et al. Recent Advances and Applications of Deep Learning Methods in Materials Science[J]. npj \| Computational Materials, 2022, 8(1): 59.
22	O'Shea T J, West N. Radio Machine Learning Dataset Generation with GNU Radio[C]//Proceedings of the 6th GNU Radio Conference. Tempe, AZ: GNURadio, 2016.
23	Xuan Qi, Li Xiaohui, Chen Zhuangzhi, et al. Deep Transfer Clustering of Radio Signals[EB/OL]. (2021-07-26) [2023-02-19]. .
24	Woo Sanghyun, Park Jongchan, Lee J Y, et al. CBAM: Convolutional Block Attention Module[C]//Computer Vision-ECCV 2018. Cham: Springer International Publishing, 2018: 3-19.
25	Lee Haeyun, Park Jinhyoung, Jae Youn Hwang. Channel Attention Module with Multiscale Grid Average Pooling for Breast Cancer Segmentation in an Ultrasound Image[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2020, 67(7): 1344-1353.
26	Du Miao, Yu Qin, Fei Shaomin, et al. Fully Dense Neural Network for the Automatic Modulation Recognition[EB/OL]. (2019-12-07) [2023-03-07]. .

参数	RGFCNN1	RGFCNN2	RGFCNN3	RGFCNN4
输入数据尺寸	(128, 2)	(128, 2)	(128, 2)	(128, 2)
透射层数目	3	3	3	3
元素分组单元尺寸	(4, 2)	(8, 2)	(16, 2)	(32, 2)
元素分组全连接隐藏节点数	4	8	16	32
总的隐藏节点数	128	128	128	128
训练参数量	4 113	4 423	6 423	10 575

参数	FCNN	RGFCNN
输入数据尺寸	(128, 2)	(128, 2)
透射层数目		3
元素分组单元尺寸		(16, 2)
元素分组全连接隐藏节点数		16
总的隐藏节点数	128	128
训练参数量	34 315	6 423

网络名称	分组节点数	总节点数	参数量
FCNN-64	/	64	17 163
RGFCNN-64	8	64	3 607
FCNN-128	/	128	34 315
RGFCNN-128	16	128	6 423
FCNN-256	/	256	68 619
RGFCNN-256	32	256	12 055
FCNN-512	/	512	137 227
RGFCNN-512	64	512	23 319
FCNN-1024	/	1 024	274 443
RGFCNN-1024	128	1 024	45 847

[1]	Yu Du, Xinquan Yang, Jianhua Zhang, Suchun Yuan, Huachao Xiao, Jingjing Yuan. Modulation Recognition Method of Mixed Signal Based on Intelligent Analysis of Cyclic Spectrum Section [J]. Journal of System Simulation, 2023, 35(1): 146-157.
[2]	Jiang Li, Zhao Guoqing, Li Lin. Method for Modulation Recognition of Radar Signals in Complex Environment [J]. Journal of System Simulation, 2016, 28(5): 997-1002.