Blind detection of M-ary quaternary phase shift keying signals by a complex Hopfield neural network with amplitude-phase-type hard-multistate-activation-function

Zhang Yun; Zhang Zhi-Yong; Yu Shu-Juan

doi:10.7498/aps.61.140701

Abstract

Considering the disadvantage of the algorithms based on statistics, a novel algorithm based on complex Hopfield neural network with amplitude-phase-type hard-multistate-activation-function (CHNN-APHM) is proposed to detect M-ary quaternary phase shift keying (MPSK) signals blindly. An amplitude-phase-type hard-multistate-activation-function is constructed. The stabilities of the CHNN-APHM with asynchronous and synchronous operating mode are also analyzed separately. While the weighted matrix of CHNN-APHM is constructed by the complementary projection operator of received signals, the problem of quadratic optimization with integer constraints can be successfully solved with the CHNN-APHM, and the MPSK signals are blindly detected. Simulation results show that the algorithm reaches the real equilibrium points with shorter received signals and if is applicable for channel with common zeros.

Keywords:

Authors and contacts

1.
College of Electronic Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;
2.
College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Funds: Project supported by the National Natural Science Foundation of China (Grant No. 60772060).

References

[1]	Jankowski S, Lozowski A, Zurada J M 1996 IEEE Trans. Neural Networks 7 1491
[2]	Muezzinoglu M K, Guzelics C, Zurada J M 2003 IEEE Trans. Neural Networks 14 891
[3]	Zhou W, Zurada J M 2009 IEEE Trans. Circuits and Systems-II: Express Briefs 56 669
[4]	Zhou W, Zurada J M 2009 Neurocomputing 72 3782
[5]	Lee D L 2006 IEEE Trans. Neural Networks 17 1341
[6]	Zurada J M, Cloete I, Poe1 E 1996 Neurocomputing 13 135
[7]	Zurada J M 2000 Proc. of the 30th IEEE International Symposium on Multiple-Valued Logic Portland, Oregon, May 23-25, 2000 p67
[8]	Zhang Y, Zhang Z Y 2011 Acta Phys. Sin. 60 090703 (in Chinese) [张昀, 张志涌 2011 物理学报 60 090703]

Cited By

[1]	Jankowski S, Lozowski A, Zurada J M 1996 IEEE Trans. Neural Networks 7 1491
[2]	Muezzinoglu M K, Guzelics C, Zurada J M 2003 IEEE Trans. Neural Networks 14 891
[3]	Zhou W, Zurada J M 2009 IEEE Trans. Circuits and Systems-II: Express Briefs 56 669
[4]	Zhou W, Zurada J M 2009 Neurocomputing 72 3782
[5]	Lee D L 2006 IEEE Trans. Neural Networks 17 1341
[6]	Zurada J M, Cloete I, Poe1 E 1996 Neurocomputing 13 135
[7]	Zurada J M 2000 Proc. of the 30th IEEE International Symposium on Multiple-Valued Logic Portland, Oregon, May 23-25, 2000 p67
[8]	Zhang Y, Zhang Z Y 2011 Acta Phys. Sin. 60 090703 (in Chinese) [张昀, 张志涌 2011 物理学报 60 090703]

[1]	Zhang Yu, Liu Rui-Wen, Zhang Jing-Yang, Jiao Bin-Bin, Wang Ru-Zhi. Gallium oxide cantilevered thin film-based solar-blind photodetector and its arc detection applications. Acta Physica Sinica, 2024, 0(0): 0-0. doi: 10.7498/aps.73.20240186
[2]	Zhao Wei-Rui, Wang Hao, Zhang Lu, Zhao Yue-Jin, Chu Chun-Yan. High-precision co-phase method for segments based on a convolutional neural network. Acta Physica Sinica, 2022, 71(16): 164202. doi: 10.7498/aps.71.20220434
[3]	Li Guang-Ming, Hu Zhi-Hui. Blind chaotic signal extraction based on artificial bee colony algorithm. Acta Physica Sinica, 2016, 65(23): 230501. doi: 10.7498/aps.65.230501
[4]	Yang Yang, Li Xiu-Kun. Blind source extraction based on time-frequency characteristics for underwater object acoustic scattering. Acta Physica Sinica, 2016, 65(16): 164301. doi: 10.7498/aps.65.164301
[5]	Zeng Zhe-Zhao, Zhou Yong, Hu Kai. Study on partial discharge signals detection by extended Duffing oscillator. Acta Physica Sinica, 2015, 64(7): 070505. doi: 10.7498/aps.64.070505
[6]	Huang Jin-Wang, Feng Jiu-Chao, Lü Shan-Xiang. Blind source separation of chaotic signals in wireless sensor networks. Acta Physica Sinica, 2014, 63(5): 050502. doi: 10.7498/aps.63.050502
[7]	Yu Shu-Juan, Huan Ru-Song, Zhang Yun, Feng Di. Novel improved blind detection algorithms based on chaotic neural networks. Acta Physica Sinica, 2014, 63(6): 060701. doi: 10.7498/aps.63.060701
[8]	Wang Shi-Yuan, Feng Jiu-Chao. A novel method of estimating parameter and its application to blind separation of chaotic signals. Acta Physica Sinica, 2012, 61(17): 170508. doi: 10.7498/aps.61.170508
[9]	Tang Liang-Rui, Fan Bing, Kang Zhong-Miao. A chaos synchronization method based on amplitude. Acta Physica Sinica, 2012, 61(8): 080508. doi: 10.7498/aps.61.080508
[10]	Xu Jian, Chen Xiao-Yu, Li Hai-Tao. Determing the entanglement of quantum nonbinary graph states. Acta Physica Sinica, 2012, 61(22): 220304. doi: 10.7498/aps.61.220304
[11]	Li Cheng, Shi Dan, Zou Yun-Ping. A feedback neural network with weights of sinusoidal functions. Acta Physica Sinica, 2012, 61(7): 070701. doi: 10.7498/aps.61.070701
[12]	Gong Yun-Rui, He Di, He Chen. Investigation of blind detection mechanism for chaotic UWB system based on generalized negentrogy. Acta Physica Sinica, 2012, 61(12): 120502. doi: 10.7498/aps.61.120502
[13]	Zhang Yun, Zhang Zhi-Yong. Blind detection algorithm of complex multi-valued discrete Hopfield network. Acta Physica Sinica, 2011, 60(9): 090703. doi: 10.7498/aps.60.090703
[14]	Feng Jian, Liu Jin-Hai, Zhang Hua-Guang. Real-time novelty detector of chaotic time series based on retina neural network. Acta Physica Sinica, 2010, 59(7): 4472-4479. doi: 10.7498/aps.59.4472
[15]	Wu Ran-Chao. Synchronization of delayed discrete-time neural networks. Acta Physica Sinica, 2009, 58(1): 139-142. doi: 10.7498/aps.58.139
[16]	Li Xue-Xia, Feng Jiu-Chao. A blind separation method for chaotic signals. Acta Physica Sinica, 2007, 56(2): 701-706. doi: 10.7498/aps.56.701
[17]	Xing Hong-Yan, Xu Wei. The neural networks method for detecting weak signals under chaotic background. Acta Physica Sinica, 2007, 56(7): 3771-3776. doi: 10.7498/aps.56.3771
[18]	Xiong Tao, Zhang Bian-Li, Chang Sheng-Jiang, Shen Jin-Yuan, Zhang Yan-Xin. A multiple input-queued ATM switching fabric based on hopfield neural network cell scheduling. Acta Physica Sinica, 2005, 54(5): 2435-2440. doi: 10.7498/aps.54.2435
[19]	Wang Fu-Peng, Wang Zan-Ji, Guo Jing-Bo. . Acta Physica Sinica, 2002, 51(3): 474-481. doi: 10.7498/aps.51.474
[20]	MA YU-QIANG, ZHANG YUE-MING, GONG CHANG-DE. RETRIEVAL PROPERTIES OF HOPFIELD NEURAL NETWORK MODELS. Acta Physica Sinica, 1993, 42(8): 1356-1360. doi: 10.7498/aps.42.1356

Catalog

Vol. 61, Issue 14 - 2012-07-20

Metrics

Abstract views: 6504
PDF Downloads: 465
Cited By: 0

姓名
邮箱
手机号码
标题
留言内容
验证码

Search

Article

留言板