An Improved Set-Membership Proportionate Adaptive Algorithm for a Block-Sparse System
College of Information and Communications Engineering, Harbin Engineering University, Harbin 150001, China
College of Communication and Electronic Engineering, Qiqihar University, Qiqihar 161006, China
National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China
Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, China
Tencent AI Lab, Bellevue, WA 98004, USA
Author to whom correspondence should be addressed.
Received: 8 February 2018 / Revised: 12 March 2018 / Accepted: 14 March 2018 / Published: 19 March 2018
In this paper, an improved set-membership proportionate normalized least mean square (SM-PNLMS) algorithm is proposed for block-sparse systems. The proposed algorithm, which is named the block-sparse SM-PNLMS (BS-SMPNLMS), is implemented by inserting a penalty of a mixed
norm of weight-taps into the cost function of the SM-PNLMS. Furthermore, an improved BS-SMPNLMS algorithm (the (BS-SMIPNLMS algorithm) is also derived and analyzed. The proposed algorithms are well investigated in the framework of network echo cancellation. The results of simulations indicate that the devised BS-SMPNLMS and BS-SMIPNLMS algorithms converge faster and have smaller estimation errors compared with related algorithms.
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MDPI and ACS Style
Jin, Z.; Li, Y.; Liu, J. An Improved Set-Membership Proportionate Adaptive Algorithm for a Block-Sparse System. Symmetry 2018, 10, 75.
Jin Z, Li Y, Liu J. An Improved Set-Membership Proportionate Adaptive Algorithm for a Block-Sparse System. Symmetry. 2018; 10(3):75.
Jin, Zhan; Li, Yingsong; Liu, Jianming. 2018. "An Improved Set-Membership Proportionate Adaptive Algorithm for a Block-Sparse System." Symmetry 10, no. 3: 75.
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