Phase Self-amending Blind Equalization Algorithm Using Feedforward Neural Network for High-Order QAM Signals in Underwater Acoustic Channels

Autor:	Zhong Liu, Yasong Luo, Xuezhi Fu, Pengfei Peng
Rok vydání:	2009
Předmět:	business.industry Computer science Communications system QAM Deflection (engineering) Feedforward neural network High order Underwater Telecommunications business Algorithm Underwater acoustic communication Blind equalization Communication channel
Zdroj:	Advances in Neural Networks – ISNN 2009 ISBN: 9783642015120 ISNN (3)
DOI:	10.1007/978-3-642-01513-7_59
Popis:	Complex-valued and non-constant modulus signals are widely used in modern high-speed underwater acoustic communication systems. Based on this environment, a complex-valued blind equalization algorithm using feedforward neural network is brought forward. Aiming at the defects that traditional constant modulus equalization algorithm can't rectify the phase deflection, the cost function is reformed and also a new modified constant modulus algorithm is given. Besides, the new algorithm is improved by introducing the square decision technique to achieve better convergence speed and less gurgitation. The results of simulation show that this new equalization algorithm not only has the ability of phase self-amending, but also performs better than traditional algorithm in the ability and speed of convergence in high order QAM communication systems.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::07d8d03a2c9a502ef7b44194f14634b3 https://doi.org/10.1007/978-3-642-01513-7_59 Zobrazit plný text záznamu