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
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[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 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 60 090703]

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Catalog

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

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