@inproceedings{041587ea3d6b481fbace38744caee389,
title = "Sparse NLMS adaptive algorithms for multipath wireless channel estimation",
abstract = "Embedding a sparse penalty in conventional Least Mean Square (LMS) adaptive algorithms is an established strategy to enhance the performance and robustness against noise in the estimation of sparse plants, such as wireless mul-tipath channels. In this paper we review the most prominent NLMS-based algorithms with ℓp-norm constraint, discussing the underlying mechanisms that lead to improvement gains in sparse scenarios. Simulation results validate the analysis and comparative discussion. Given that adaptive algorithms operating in time domain deteriorate with correlated signals, we propose hereby a novel frequency-domain (FD) ℓP-NLMS that performs in such situations. Simulation results indicate that the proposed method outperforms its time-domain counterparts not only in convergence rate but more importantly in residual misalignment. This important result has not been echoed so far.",
keywords = "Frequency-Domain, Least Mean Square (LMS), Normalized LMS, Sparsity, Time-Domain, ℓ1-norm, ℓp-norm",
author = "Abdullah Al-Shabili and Bilal Taha and Hadeel Elayan and Fatima Al-Ogaili and Leen Alhalabi and Luis Weruaga and Shihab Jimaa",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 11th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2015 ; Conference date: 19-10-2015 Through 21-10-2015",
year = "2015",
month = dec,
day = "4",
doi = "10.1109/WiMOB.2015.7348049",
language = "British English",
series = "2015 IEEE 11th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "839--844",
booktitle = "2015 IEEE 11th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2015",
address = "United States",
}