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Zero attracting recursive least square algorithms

Zero attracting recursive least square algorithms
Zero attracting recursive least square algorithms
The $l_1$-norm sparsity constraint is a widely used technique for constructing sparse models. In this contribution, two zero-attracting recursive least squares algorithms, referred to as ZA-RLS-I and ZA-RLS-II, are derived by employing the $l_1$-norm of parameter vector constraint to facilitate the model sparsity. In order to achieve a closed-form solution, the $l_1$-norm of the parameter vector is approximated by an adaptively weighted $l_2$-norm, in which the weighting factors are set as the inversion of the associated $l_1$-norm of parameter estimates that are readily available in the adaptive learning environment. ZA-RLS-II is computationally more efficient than ZA-RLS-I by exploiting the known results from linear algebra as well as the sparsity of the system. The proposed algorithms are proven to converge, and adaptive sparse channel estimation is used to demonstrate the effectiveness of the proposed approach.
0018-9545
213-221
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Gao, Junbin
a3dcab84-9675-402c-a19e-d41ea9973f3a
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Gao, Junbin
a3dcab84-9675-402c-a19e-d41ea9973f3a
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80

Hong, Xia, Gao, Junbin and Chen, Sheng (2017) Zero attracting recursive least square algorithms. IEEE Transactions on Vehicular Technology, 66 (1), 213-221. (doi:10.1109/TVT.2016.2533664).

Record type: Article

Abstract

The $l_1$-norm sparsity constraint is a widely used technique for constructing sparse models. In this contribution, two zero-attracting recursive least squares algorithms, referred to as ZA-RLS-I and ZA-RLS-II, are derived by employing the $l_1$-norm of parameter vector constraint to facilitate the model sparsity. In order to achieve a closed-form solution, the $l_1$-norm of the parameter vector is approximated by an adaptively weighted $l_2$-norm, in which the weighting factors are set as the inversion of the associated $l_1$-norm of parameter estimates that are readily available in the adaptive learning environment. ZA-RLS-II is computationally more efficient than ZA-RLS-I by exploiting the known results from linear algebra as well as the sparsity of the system. The proposed algorithms are proven to converge, and adaptive sparse channel estimation is used to demonstrate the effectiveness of the proposed approach.

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Accepted/In Press date: 20 February 2016
e-pub ahead of print date: 24 February 2016
Published date: January 2017
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 404853
URI: http://eprints.soton.ac.uk/id/eprint/404853
ISSN: 0018-9545
PURE UUID: 3fb882aa-ebe7-4625-a094-2c10679bf210

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Date deposited: 24 Jan 2017 13:11
Last modified: 16 Dec 2019 19:30

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