TY - GEN
T1 - Sparse channel estimation using adaptive filtering and compressed sampling
AU - Khalifa, Mohammed Osman
AU - Abdelhafiz, Abubaker Hassan
AU - Zerguine, Azzedine
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - 10-LMS
KW - Adaptive Filtering
KW - Compressed Sampling
KW - LMS Algorithm
KW - Least-Squares (LS) Estimation
KW - Sparse Communication Channels
UR - https://www.scopus.com/pages/publications/84889593501
U2 - 10.1109/ICCEEE.2013.6633922
DO - 10.1109/ICCEEE.2013.6633922
M3 - Conference contribution
AN - SCOPUS:84889593501
SN - 9781467362313
T3 - Proceedings - 2013 International Conference on Computer, Electrical and Electronics Engineering: 'Research Makes a Difference', ICCEEE 2013
SP - 144
EP - 147
BT - Proceedings - 2013 International Conference on Computer, Electrical and Electronics Engineering
ER -