TY - GEN
T1 - Robust adaptive sparse channel estimation in the presence of impulsive noises
AU - Gui, Guan
AU - Xu, Li
AU - Ma, Wentao
AU - Chen, Badong
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/9
Y1 - 2015/9/9
N2 - Broadband wireless channels usually have the sparse nature. Based on the assumption of Gaussian noise model, adaptive filtering algorithms for reconstructing sparse channels were proposed to take advantage of channel sparsity. However, impulsive noises are often existed in many advanced broadband communications systems. These conventional algorithms are vulnerable to performance deteriorate by the impulsive noise. In this paper, sign least mean square algorithm (SLMS) based robust sparse adaptive filtering algorithms are proposed to estimate channels as well as to mitigate impulsive noise. By using different sparsity-inducing penalty functions, i.e., zero-attracting (ZA), reweighted ZA (RZA), reweighted L1-norm (RL1) and Lp-norm (LP), the proposed SLMS algorithms are termed as SLMS-ZA, SLMS-RZA, LSMS-RL1 and SLMS-LP. Simulation results are given to validate the proposed algorithms.
AB - Broadband wireless channels usually have the sparse nature. Based on the assumption of Gaussian noise model, adaptive filtering algorithms for reconstructing sparse channels were proposed to take advantage of channel sparsity. However, impulsive noises are often existed in many advanced broadband communications systems. These conventional algorithms are vulnerable to performance deteriorate by the impulsive noise. In this paper, sign least mean square algorithm (SLMS) based robust sparse adaptive filtering algorithms are proposed to estimate channels as well as to mitigate impulsive noise. By using different sparsity-inducing penalty functions, i.e., zero-attracting (ZA), reweighted ZA (RZA), reweighted L1-norm (RL1) and Lp-norm (LP), the proposed SLMS algorithms are termed as SLMS-ZA, SLMS-RZA, LSMS-RL1 and SLMS-LP. Simulation results are given to validate the proposed algorithms.
KW - alpha-stable noise model
KW - sign least mean square (SLMS)
KW - sparse adaptive channel estimation
KW - sparsity-inducing penalty
UR - https://www.scopus.com/pages/publications/84961301686
U2 - 10.1109/ICDSP.2015.7251950
DO - 10.1109/ICDSP.2015.7251950
M3 - 会议稿件
AN - SCOPUS:84961301686
T3 - International Conference on Digital Signal Processing, DSP
SP - 628
EP - 632
BT - 2015 IEEE International Conference on Digital Signal Processing, DSP 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE International Conference on Digital Signal Processing, DSP 2015
Y2 - 21 July 2015 through 24 July 2015
ER -