Title :
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
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;
Conference_Titel :
Computing, Electrical and Electronics Engineering (ICCEEE), 2013 International Conference on
Conference_Location :
Khartoum
Print_ISBN :
978-1-4673-6231-3
DOI :
10.1109/ICCEEE.2013.6633922
