TY - GEN
T1 - Regularization selection method for LMS-type sparse multipath channel estimation
AU - Huang, Zhengxing
AU - Gui, Guan
AU - Huang, Anmin
AU - Xiang, Dong
AU - Adachi, Fumiyki
PY - 2013
Y1 - 2013
N2 - Least mean square (LMS)-type adaptive sparse algorithms have been attracting much attention on sparse multipath channel estimation (SMPC) due to their two advantages: low computational complexity and reliability. By introducing ℓ1 -norm sparse constraint function into LMS algorithm, both zero-attracting least mean square (ZA-LMS) and reweighted zero-attracting least mean square (RZA-LMS) have been proposed for SMPC. It is well known that the performance of the SMPC is decided by regularization parameter which balances channel estimation error and sparse penalty strength. However, optimal regularization parameter selection has not yet considered in the two proposed algorithms. Based on the compressive sensing theory, in this paper, we explain the mathematical relationship between Lasso and LMS-type adaptive sparse algorithms. Later, an approximate optimal regulation parameter selection method is proposed for ZA-LMS and RZA-LMS, respectively. Monte Carlo based computer simulations are presented to show the effectiveness of our propose method.
AB - Least mean square (LMS)-type adaptive sparse algorithms have been attracting much attention on sparse multipath channel estimation (SMPC) due to their two advantages: low computational complexity and reliability. By introducing ℓ1 -norm sparse constraint function into LMS algorithm, both zero-attracting least mean square (ZA-LMS) and reweighted zero-attracting least mean square (RZA-LMS) have been proposed for SMPC. It is well known that the performance of the SMPC is decided by regularization parameter which balances channel estimation error and sparse penalty strength. However, optimal regularization parameter selection has not yet considered in the two proposed algorithms. Based on the compressive sensing theory, in this paper, we explain the mathematical relationship between Lasso and LMS-type adaptive sparse algorithms. Later, an approximate optimal regulation parameter selection method is proposed for ZA-LMS and RZA-LMS, respectively. Monte Carlo based computer simulations are presented to show the effectiveness of our propose method.
KW - adaptive sparse channel estimation
KW - least mean square (LMS)
KW - regularization parameter selection
KW - reweighted zero-attracting least mean square (RZA-LMS)
KW - zero-attracting least mean square (ZA-LMS)
UR - http://www.scopus.com/inward/record.url?scp=84902329909&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84902329909&partnerID=8YFLogxK
U2 - 10.1109/APCC.2013.6766029
DO - 10.1109/APCC.2013.6766029
M3 - Conference contribution
AN - SCOPUS:84902329909
SN - 9781467360500
T3 - 2013 19th Asia-Pacific Conference on Communications, APCC 2013
SP - 649
EP - 654
BT - 2013 19th Asia-Pacific Conference on Communications, APCC 2013
PB - IEEE Computer Society
T2 - 2013 19th Asia-Pacific Conference on Communications, APCC 2013
Y2 - 29 August 2013 through 31 August 2013
ER -