TY - CONF
T1 - Blind sparse channel identification using subspace-based algorithm
AU - Jimenez, Nicthe Nataly
AU - Fernandez-Vazquez, Alfonso
AU - Dolecek, Gordana Jovanovic
PY - 2017/3/2
Y1 - 2017/3/2
N2 - © 2016 IEEE. This paper addresses the problem of blind channel identification under sparse channel condition. Our approach is an extension of the subspace blind channel identification methods. Unlike previous approaches for blind channel identification where the optimization is in least square sense, i.e., the L2 norm, the proposed extension includes the identification of sparse channels and uses the L1 norm. By doing so, we show that the performance of the proposed method outperforms previous approach, under sparse channel conditions. Numerical examples are included in order to demonstrate the effectiveness of the proposed approach. Bit Error Rate and normalized error performances of our approach are also included.
AB - © 2016 IEEE. This paper addresses the problem of blind channel identification under sparse channel condition. Our approach is an extension of the subspace blind channel identification methods. Unlike previous approaches for blind channel identification where the optimization is in least square sense, i.e., the L2 norm, the proposed extension includes the identification of sparse channels and uses the L1 norm. By doing so, we show that the performance of the proposed method outperforms previous approach, under sparse channel conditions. Numerical examples are included in order to demonstrate the effectiveness of the proposed approach. Bit Error Rate and normalized error performances of our approach are also included.
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U2 - 10.1109/MWSCAS.2016.7870054
DO - 10.1109/MWSCAS.2016.7870054
M3 - Paper
T2 - Midwest Symposium on Circuits and Systems
Y2 - 2 March 2017
ER -