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

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Advances in Neural Networks – ISNN 2009 (ISNN 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5553))

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Abstract

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.

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© 2009 Springer-Verlag Berlin Heidelberg

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Luo, Y., Liu, Z., Peng, P., Fu, X. (2009). Phase Self-amending Blind Equalization Algorithm Using Feedforward Neural Network for High-Order QAM Signals in Underwater Acoustic Channels. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_59

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  • DOI: https://doi.org/10.1007/978-3-642-01513-7_59

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01512-0

  • Online ISBN: 978-3-642-01513-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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