Mehboob, A, Zhang, L, Khangosstar, J et al. (1 more author) (2013) Joint channel and impulsive noise estimation for OFDM based power line communication systems using compressed sensing. In: Power Line Communications and Its Applications (ISPLC), 2013 17th IEEE International Symposium on. 17th IEEE International Symposium on Power Line Communications and Its Applications, 24-27 Mar 2013, Johannesburg, South Africa. Johannesburg, South Africa , 203 - 208. ISBN 978-1-4673-6014-2
Abstract
Compressed sensing (CS) based joint channel impulse response (CIR) and impulsive noise (IN) estimation is proposed for OFDM systems. Current literature considers CS based CIR and IN estimation as two separate problems. We show that the CIR and IN estimation can be formulated as a single joint problem where the probability of overlap between the supports of IN and CIR in an OFDM frame is significantly low. This allows us to assume that the IN and CIR supports are disjoint. Furthermore, we show that the CS solution from the joint formulation provides improvement in the estimation of CIR and IN as compared to the separate schemes. Numerical simulations verify the improvements in terms of Mean Square Error (MSE), Bit Error Rate (BER) or spectral efficiency offered by the proposed (joint) scheme.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | channel estimation; compressive sensing; impulsive noise mitigation; joint channel and impulsive noise estimation |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 21 May 2015 14:44 |
Last Modified: | 20 Oct 2015 13:01 |
Published Version: | http://dx.doi.org/10.1109/ISPLC.2013.6525850 |
Status: | Published |
Publisher: | Johannesburg, South Africa |
Identification Number: | 10.1109/ISPLC.2013.6525850 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:84943 |