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An analysis of real-fourier domain-based adaptive algorithms implemented with the Hartley transform using cosine-sine symmetries
K. M.Muraleedhara Prabhu
Published in
2005
Volume: 53
   
Issue: 2 I
Pages: 622 - 629
Abstract
The least mean squared (LMS) algorithm and its variants have been the most often used algorithm in adaptive signal processing. However the LMS algorithm suffers from a high computational complexity, especially with large filter lengths. The Fourier transform-based block normalized LMS (FBNLMS) reduces the computation count by using the discrete Fourier transform (DFT) and exploiting the fast algorithms for implementing the DFT. Even though the savings achieved with the FBNLMS over the direct-LMS implementation are significant, the computational requirements of FBNLMS are still very high, rendering many real-time applications, like audio and video estimation, infeasible. The Hartley transform-based BNLMS (HBNLMS) is found to have a computational complexity much less than, and a memory requirement almost of the same order as, that of the FBNLMS. This paper is based on the cosine and sine symmetric implementation of the discrete Hartley transform (DHT), which is the key in reducing the computational complexity of the FBNLMS by 33% asymptotically (with respect to multiplications). The parallel implementation of the discrete cosine transform (DCT) in turn can lead to more efficient implementations of the HBNLMS. © 2005 IEEE.
About the journal
JournalIEEE Transactions on Signal Processing
ISSN1053587X
Open AccessYes
Concepts (13)
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    Adaptive algorithms
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    Computational complexity
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    Convolution
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    Cosine transforms
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    Discrete fourier transforms
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    Frequency domain analysis
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    Matrix algebra
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    Adaptive signal processing
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    DISCRETE SINE TRANSFORM (DST)
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    FOURIER TRANSFORM-BASED BLOCK NORMALIZED LMS (FBNLMS)
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    FREQUENCY DOMAIN ALGORITHMS
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    LEAST MEAN SQUARE (LMS) ALGORITHMS
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    Adaptive filtering