• Title/Summary/Keyword: A self-generated symbol set

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Complex-Channel Blind Equalization Using Cross-Correntropy (상호 코렌트로피를 이용한 복소 채널 블라인드 등화)

  • Kim, Nam-Yong
    • Journal of Internet Computing and Services
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    • v.11 no.5
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    • pp.19-26
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    • 2010
  • The criterionmaximizing cross-correntropy (MCC) of two different random variables has yielded superior performance comparing to mean squared error criterion. In this paper we present a complex-valued blind equalizer algorithm for QAM and complex channel environments based on cross-correntropy criterion which uses, as two variables, equalizer output PDF and Parzen PDF estimate of a self-generated symbol set. Simulation results show significantly enhanced performance of symbol-point concentration with no phase rotation in complex-channel communication.

Complex-Channel Blind Equalization using Euclidean Distance Algorithms with a Self-generated Symbol Set and Kernel Size Modification (자가 발생 심볼열과 커널 사이즈 조절을 통한 유클리드 거리 알고리듬의 복소 채널 블라인드 등화)

  • Kim, Nam-Yong
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.36 no.1A
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    • pp.35-40
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    • 2011
  • The complex-valued blind algorithm based on a set of randomly generated symbols and Euclidean distance can take advantage of information theoretic learning and cope with the channel phase rotation problems. On the algorithm, in this paper, the effect of kernel size has been studied and a kernel-modified version of the algorithm that rearranges the forces between the information potentials by kernel-modification has been proposed. In simulation results for 16 QAM and complex-channel models, the proposed algorithm show significantly enhanced performance of symbol-point concentration and no phase rotation problems caused by the complex channel models.

A Study on the Complex-Channel Blind Equalization Using ITL Algorithms

  • Kim, Nam-Yong
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.35 no.8A
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    • pp.760-767
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    • 2010
  • For complex channel blind equalization, this study presents the performance and characteristics of two complex blind information theoretic learning algorithms (ITL) which are based on minimization of Euclidian distance (ED) between probability density functions compared to constant modulus algorithm which is based on mean squared error (MSE) criterion. The complex-valued ED algorithm employing constant modulus error and the complex-valued ED algorithm using a self-generated symbol set are analyzed to have the fact that the cost function of the latter forces the output signal to have correct symbol values and compensate amplitude and phase distortion simultaneously without any phase compensation process. Simulation results through MSE convergence and constellation comparison for severely distorted complex channels show significantly enhanced performance of symbol-point concentration with no phase rotation.