One-step Calculation Circuit of Blind Signal Detection using Complex-valued Hopfield Neural Network

HONG Qinghui; SUN Chen; XIAO Pingdan; WEI Zhengmiao; DU Sichun

doi:10.11999/JEIT240224

Volume 46 Issue 11

Nov. 2024

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(11): 4123-4131

HONG Qinghui, SUN Chen, XIAO Pingdan, WEI Zhengmiao, DU Sichun. One-step Calculation Circuit of Blind Signal Detection using Complex-valued Hopfield Neural Network[J]. Journal of Electronics & Information Technology, 2024, 46(11): 4123-4131. doi: 10.11999/JEIT240224

Citation:

HONG Qinghui, SUN Chen, XIAO Pingdan, WEI Zhengmiao, DU Sichun. One-step Calculation Circuit of Blind Signal Detection using Complex-valued Hopfield Neural Network[J]. Journal of Electronics & Information Technology, 2024, 46(11): 4123-4131. doi: 10.11999/JEIT240224

Citation:

PDF( 6125 KB)

One-step Calculation Circuit of Blind Signal Detection using Complex-valued Hopfield Neural Network

doi: 10.11999/JEIT240224

College of Information Science and Engineering, Hunan University, Changsha 410082, China

Funds: The National Natural Science Foundation of China (62234008, 62371186), Huxiang Young Talents Project (2023RC3103), The Natural Science Foundation of Hunan Province(2023JJ30168, 2022JJ30160, 2021JJ40111), The National Key R&D Program of China (2022YFB3903800)

Received Date: 2024-03-29
Rev Recd Date: 2024-10-10

Available Online: 2024-10-16

Publish Date: 2024-11-01

Abstract

Abstract

Blind signal detection is of great significance in large-scale communication networks and has been widely used. How to quickly obtain blind signal detection results is an urgent need for the new generation of real-time communication networks. Considering this demand, a Complex-valued Hopfield Neural Network (CHNN) circuit is designed that can accelerate blind signal detection from an analog circuit perspective, the proposed circuit can accelerate the blind signal detection by rapidly performing massively parallel calculation in one step. At the same time, the circuit can be programmable by adjusting the conductance and input voltage of the memristor. The Pspice simulation results show that the computing accuracy of the proposed circuit can exceed 99%. Compared with Matlab software simulation, the proposed circuit is three orders of magnitude faster in terms of computing time. And the accuracy can be maintained at more than 99% even under the interference of 20% noise.
- Circuit design,
- Memristor,
- Complex-valued Hopfield Neural Network(CHNN),
- Blind signal detection

FullText(HTML)

References(22)

References

[1]	SATO Y. A method of self-recovering equalization for multilevel amplitude-modulation systems[J]. IEEE Transactions on Communications, 1975, 23(6): 679–682. doi: 10.1109/TCOM.1975.1092854.
[2]	TONG Lang, XU Guanghan, and KAILATH T. Blind identification and equalization based on second-order statistics: A time domain approach[J]. IEEE Transactions on Information Theory, 1994, 40(2): 340–349. doi: 10.1109/18.312157.
[3]	LI Jin, FENG Dazheng, and LI Bingbing. Space-time blind equalization of dispersive MIMO systems driven by QAM signals[J]. IEEE Transactions on Vehicular Technology, 2018, 67(5): 4136–4148. doi: 10.1109/TVT.2018.2790432.
[4]	HU Rong, YU Shujuan, ZHANG Yun, et al. WSN blind detection based on SOS[C]. The 11th World Congress on Intelligent Control and Automation, Shenyang, China, 2014: 5763–5766. doi: 10.1109/WCICA.2014.7053704.
[5]	LV Zhisheng, FENG Bin, and TAN Li. An improved dual-mode blind equalization algorithm for QAM signals[C]. 2022 6th International Conference on Robotics and Automation Sciences (ICRAS), Wuhan, China, 2022: 283–287. doi: 10.1109/ICRAS55217.2022.9842274.
[6]	BLOCHBERGER M, ELVANDER F, ALI R, et al. Distributed cross-relation-based frequency-domain blind system identification using online-admm[C]. 2022 International Workshop on Acoustic Signal Enhancement (IWAENC), Bamberg, Germany, 2022: 1–5. doi: 10.1109/IWAENC53105.2022.9914714.
[7]	YUAN Kang, ZHUO Junnan, GAO Wei, et al. Diffusion constant modulus algorithm for blind equalization[C]. 2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Xi’an, China, 2022: 1–6. doi: 10.1109/ICSPCC55723.2022.9984630.
[8]	XIAO Ying and DONG Yuhua. Blind equalization based on neural network under LS criterion by gradient iteration algorithm[C]. 2009 Fifth International Conference on Natural Computation, Tianjian, China, 2009: 91–94. doi: 10.1109/ICNC.2009.134.
[9]	ZHANG Yun and ZHANG Zhiyong. Blind 8PSK signals detection using discrete complex-valued Hopfield network[C]. 2010 IEEE 12th International Conference on Communication Technology, Nanjing, China, 2010: 983–986. doi: 10.1109/ICCT.2010.5688787.
[10]	WU Jinwen, JIN Haihong, YU Shujuan, et al. A novel blind detection algorithm based on multi-clustering complex neural network[C]. Proceedings of 2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Chongqing, China, 2021: 1017–1021. doi: 10.1109/IMCEC51613.2021.9482354.
[11]	MEI Ruru, WANG Zhugang, and HU Wanru. Robust blind equalization algorithm using convolutional neural network[J]. IEEE Signal Processing Letters, 2022, 29: 1569–1573. doi: 10.1109/LSP.2022.3189319.
[12]	XIE Xihai, FAN Chenzhao, WANG Shuai, et al. Varible step size multi-layer neural network blind equalization algorithm[C]. 2022 4th International Conference on Natural Language Processing (ICNLP), Xi’an, China, 2022: 477–481. doi: 10.1109/ICNLP55136.2022.00087.
[13]	ZHANG Hui, GU M, JIANG Xudong, et al. An optical neural chip for implementing complex-valued neural network[J]. Nature Communications, 2021, 12(1): 457. doi: 10.1038/s41467-020-20719-7.
[14]	YU Yaning and ZHANG Ziye. State estimation for complex-valued inertial neural networks with multiple time delays[J]. Mathematics, 2022, 10(10): 1725. doi: 10.3390/math10101725.
[15]	HONG Qinghui, FU Haotian, LIU Yiyang, et al. In-memory computing circuit implementation of complex-valued hopfield neural network for efficient portrait restoration[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2023, 42(10): 3338–3351. doi: 10.1109/TCAD.2023.3242858.
[16]	NISHIKAWA I, IRITANI T, and SAKAKIBARA K. Improvements of the traffic signal control by complex-valued Hopfield networks[C]. The 2006 IEEE International Joint Conference on Neural Network Proceedings, Vancouver, Canada, 2006: 459–464. doi: 10.1109/IJCNN.2006.246717.
[17]	王雷敏, 程佳俊, 胡成, 等. 基于改进的忆阻器在字符联想记忆中的应用[J]. 电子与信息学报, 2023, 45(7): 2667–2674. doi: 10.11999/JEIT220709. WANG Leimin, CHENG Jiajun, HU Cheng, et al. Application of improved memristor in character associative memory[J]. Journal of Electronics & Information Technology, 2023, 45(7): 2667–2674. doi: 10.11999/JEIT220709.
[18]	UYKAN Z. Shadow-Cuts minimization/maximization and complex Hopfield neural networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(3): 1096–1109. doi: 10.1109/TNNLS.2020.2980237.
[19]	HONG Qinghui, LI Ya, and WANG Xiaoping. Memristive continuous Hopfield neural network circuit for image restoration[J]. Neural Computing and Applications, 2020, 32(12): 8175–8185. doi: 10.1007/s00521-019-04305-7.
[20]	YAN Renao, HONG Qinghui, WANG Chunhua, et al. Multilayer memristive neural network circuit based on online learning for license plate detection[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2022, 41(9): 3000–3011. doi: 10.1109/TCAD.2021.3121347.
[21]	肖平旦, 洪庆辉, 杜四春, 等. 基于忆阻器的可编程矩阵行列式求解电路设计及应用[J]. 中国科学: 信息科学, 2023, 53(5): 1008–1025. doi: 10.1360/SSI-2022-0229. XIAO Pingdan, HONG Qinghui, DU Sichun, et al. Design and application of a programmable matrix determinant-solving circuit based on memristors[J]. SCIENTIA SINICA Informationis, 2023, 53(5): 1008–1025. doi: 10.1360/SSI-2022-0229.
[22]	YI Qian. FPGA implementation of neural network accelerator[C]. 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Xi’an, China, 2018: 1903–1906. doi: 10.1109/IMCEC.2018.8469659.