Sparse channel estimation using adaptive filtering and compressed sampling

Author

Khalifa, Mohammed Osman ; Abdelhafiz, Abubaker Hassan ; Zerguine, Azzedine

Author_Institution

Electr. Eng. Dept., King Fahd Univ. of Pet. & Miner., Dhahran, Saudi Arabia

fYear

2013

Firstpage

144

Lastpage

147

Abstract

This paper discusses the topic of estimating sparse communication channels (i.e. having mostly zero entries) using classical adaptive filtering techniques and the recently-developedmethod of compressed sampling. For this purpose, the Least-Mean Squares (LMS) a variant of it known as 10-LMS are compared with the compressed sampling technique when used to estimate a communication channels having different levels of sparsity.

Keywords

adaptive filters; channel estimation; compressed sensing; least mean squares methods; signal sampling; LMS; adaptive filtering; compressed sampling; least-mean squares; sparse communication channel estimation; Adaptive filters; Channel estimation; Compressed sensing; Estimation; Least squares approximations; Vectors; 10-LMS; Adaptive Filtering; Compressed Sampling; LMS Algorithm; Least-Squares (LS) Estimation; Sparse Communication Channels;

fLanguage

English

Publisher

ieee

Conference_Titel

Computing, Electrical and Electronics Engineering (ICCEEE), 2013 International Conference on

Conference_Location

Khartoum

Print_ISBN

978-1-4673-6231-3

Type

conf

DOI

10.1109/ICCEEE.2013.6633922

Filename

6633922

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1577765