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D. L. Wang, H. Leung, A. P. Kurian, K.-C. Kwak and H. Yoon, “A Deconvolutive Neural Network for Speech Classification with Applications to Home Service Robot,” IEEE Transactions on Instrumentation and Measurement, Vol. 59, No. 9, September 2010, pp. 1297-1303.

has been cited by the following article:

• TITLE: Nonlinear Blind Equalizers: NCMA and NMCMA

  AUTHORS: Donglin Wang, Sandeep Chandana

  KEYWORDS: Nonlinear Blind Equalizer, Nonlinear Transfer Function (NTF), CMA, Nonlinear CMA

  JOURNAL NAME: International Journal of Communications, Network and System Sciences, Vol.3 No.6, June 29, 2010

  ABSTRACT: This paper proposes two nonlinear blind equalizers: the nonlinear constant modulus algorithm (NCMA) and the nonlinear modified constant modulus algorithm (NCMA) by applying a nonlinear transfer function (NTF) into constant modulus algorithm (CMA) and modified constant modulus algorithm (MCMA), respectively. The effect of the NTF on CMA and MCMA is theoretically analyzed, which implies that the NTF can make their decision regions much sharper so that the proposed two nonlinear blind equalizers are more robust against the convergency error compared to their linear counterparts. The embedded single layer in NCMA and NMCMA simultaneously guarantees a comparably speedy convergency. On 16-quadrature amplitude modulation (QAM) symbols, computer simulations show that NCMA achieves an 8dB lower convergency mean square error (MSE) than CMA, and NMCMA achieves a 15dB lower convergency MSE than MCMA.