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Low-complexity RLS algorithms using dichotomous coordinate descent iterations

Zakharov, Yuriy V., White, George P. and Liu, Jie (2008) Low-complexity RLS algorithms using dichotomous coordinate descent iterations. IEEE Transactions on Signal Processing. pp. 3150-3161. ISSN 1053-587X

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In this paper, we derive low-complexity recursive least squares (RLS) adaptive filtering algorithms. We express the RLS problem in terms of auxiliary normal equations with respect to increments of the filter weights and apply this approach to the exponentially weighted and sliding window cases to derive new RLS techniques. For solving the auxiliary equations, line search methods are used. We first consider conjugate gradient iterations with a complexity of O(N-2) operations per sample; N being the number of the filter weights. To reduce the complexity and make the algorithms more suitable for finite precision implementation, we propose a new dichotomous coordinate descent (DCD) algorithm and apply it to the auxiliary equations. This results in a transversal RLS adaptive filter with complexity as low as 3N multiplications per sample, which is only slightly higher than the complexity of the least mean squares (LMS) algorithm (2N multiplications). Simulations are used to compare the performance of the proposed algorithms against the classical RLS and known advanced adaptive algorithms. Fixed-point FPGA implementation of the proposed DCD-based RLS algorithm is also discussed and results of such implementation are presented.

Item Type: Article
Copyright, Publisher and Additional Information: © Copyright 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Keywords: adaptive filter,conjugate gradient,DCD algorithm,dichotomous coordinate descent,FPGA implementation,line search,RLS,ADAPTIVE FILTERING ALGORITHMS,LINE SEARCH,CONVERGENCE,Signal Processing,Electrical and Electronic Engineering
Institution: The University of York
Academic Units: The University of York > Electronics (York)
Depositing User: Repository Administrator York
Date Deposited: 01 Aug 2008 13:21
Last Modified: 24 Jul 2016 00:19
Published Version: http://dx.doi.org/10.1109/TSP.2008.917874
Status: Published
Refereed: Yes
Related URLs:
URI: http://eprints.whiterose.ac.uk/id/eprint/4124

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