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Application of the CS-based Sparse Volterra Filter to the Super-RENS Disc Channel Modeling  

Moon, Woo-Sik (School of Electronic Engineering, Soongsil University)
Park, Se-Hwang (School of Electronic Engineering, Soongsil University)
Im, Sung-Bin (School of Electronic Engineering, Soongsil University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea TC / v.49, no.5, 2012 , pp. 59-65 More about this Journal
Abstract
In this paper, we investigate the compressed sensing (CS) algorithms for modeling a super-resolution near-field structure (super-RENS) disc system with a sparse Volterra filter. It is well known that the super-RENS disc system has severe nonlinear inter-symbol interference (ISI). A nonlinear system with memory can be well described with the Volterra series. Furthermore, CS can restore sparse or compressed signals from measurements. For these reasons, we employ the CS algorithms to estimate a sparse super-RENS read-out channel. The evaluation results show that the CS algorithms can efficiently construct a sparse Volterra model for the super-RENS read-out channel.
Keywords
Super-RENS; Modeling; Compressed sensing; Volterra filter;
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