Blind sparse channel identification using subspace-based algorithm

Nicthe Nataly Jimenez, Alfonso Fernandez-Vazquez, Gordana Jovanovic Dolecek

Research output: Contribution to conferencePaper


© 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.
Original languageAmerican English
StatePublished - 2 Mar 2017
EventMidwest Symposium on Circuits and Systems -
Duration: 2 Mar 2017 → …


ConferenceMidwest Symposium on Circuits and Systems
Period2/03/17 → …


Cite this

Jimenez, N. N., Fernandez-Vazquez, A., & Dolecek, G. J. (2017). Blind sparse channel identification using subspace-based algorithm. Paper presented at Midwest Symposium on Circuits and Systems, .