Adaptive Partial-Update and Sparse System Identification
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Editorial Adaptive Partial-Update and Sparse System Identification ˘ ¸ ay1 and Patrick A. Naylor2 Kutluyıl Doganc 1 School
of Electrical and Information Engineering, University of South Australia, Mawson Lakes, South Australia 5095, Australia of Electrical and Electronic Engineering, Imperial College London, Exhibition Road, London SW7 2AZ, UK
2 Department
Received 1 March 2007; Accepted 1 March 2007 Copyright © 2007 K. Do˘ganc¸ay and P. A. Naylor. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
System identification is an important task in many application areas including, for example, telecommunications, control engineering, sensing, and acoustics. It would be widely accepted that the science for identification of stationary and dynamic systems is mature. However, several new applications have recently become of heightened interest for which system identification needs to be performed on high-order moving average systems that are either sparse in the time domain or need to be estimated using sparse computation due to complexity constraints. In this special issue, we have brought together a collection of articles on recent work in this field giving specific consideration to (a) algorithms for identification of sparse systems and (b) algorithms that exploit sparseness in the coefficient update domain. The distinction between these two types of sparseness is important, as we hope will become clear to the reader in the main body of the special issue. A driving force behind the development of algorithms for sparse system identification in telecommunications has been echo cancellation in packet switched telephone networks. The increasing popularity of packet-switched telephony has led to a need for the integration of older analog systems with, for example, IP or ATM networks. Network gateways enable the interconnection of such networks and provide echo cancellation. In such systems, the hybrid echo response is delayed by an unknown bulk delay due to propagation through the network. The overall effect is, therefore, that an “active” region associated with the true hybrid echo response occurs with an unknown delay within an overall response window that has to be sufficiently long to accommodate the worst case bulk delay. In the context of network echo cancellation the direct application of well-known algorithms, such as normalized least-mean-square (NLMS), to sparse system identification gives unsatisfactory performance when the echo response is sparse. This is because the adaptive algorithm has
to operate on a long filter and the coefficient noise for nearzero-valued coefficients in the inactive regions is relatively large. To address this problem, the concept of proportionate updating was introduced. An important consideration for adaptive filters is the computational complexity that increases with the number of coefficients to be updated per sampling period. A
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