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http://dx.doi.org/10.5762/KAIS.2016.17.8.17

Self-Adaptive Performance Improvement of Novel SDD Equalization Using Sigmoid Estimate and Threshold Decision-Weighted Error  

Oh, Kil Nam (Dept. of Healthcare & Medical Engineering, Gwangju University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.17, no.8, 2016 , pp. 17-22 More about this Journal
Abstract
For the self-adaptive equalization of higher-order QAM systems, this paper proposes a new soft decision-directed (SDD) algorithm that opens the eye patterns quickly as well as significantly reducing the error level in the steady-state when it is applied to the initial equalization stage with completely closed eye patterns. The proposed method for M-QAM application minimized the computational complexity of the existing SDD by the symbol estimated based on the two symbols closest to the observation, and greatly simplified the soft decision independently of the QAM order. Furthermore, in the symbol estimating it increased the reliability of the estimates by applying the superior properties of the sigmoid function and avoiding the erroneous estimation of the threshold function. In addition, the initialization performance was improved when an error is generated to update the equalizer, weighting the symbol decision by the threshold function to the error, resulting in an extension of the range of error fluctuations. As a result, the proposed method improves remarkably the computational complexity and the properties of initialization and convergence of the traditional SDD. Through simulations for 64-QAM and 256-QAM under multipath channel conditions with additive noise, the usefulness of the proposed methods was confirmed by comparing the performance of the proposed 2-SDD and two forms of weighted 2-SDD with CMA.
Keywords
self-adaptive equalization; SDD; threshold decision; sigmoid function; symbol estimate;
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