Subband decomposition: an LMS-based algorithm to approximate the perfect reconstruction bank in the general case

Paillard, Bruno; Soumagne, J.; Mabilleau, P.; Morissette, S.

doi:10.1109/78.80794

Cited by 5 publications

(11 citation statements)

References 19 publications

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“…As we apply the backward partition scheme in (18) and (33), (B.1) can be expressed asα (n + 1) = 1 + S T Z(n + 1) Z(n + 1 − P ) Tw (n + 1) = 1 + Z T (n + 1)Z T (n + 1 − P ) Sw(n + 1) = 1 + Z T (n + 1)Z T (n + 1 − P ) w(n + 1) 0 …”

Section: Resultsmentioning

confidence: 99%

“…least mean squares (LMS) and recursive least squares (RLS) algorithms, can also be applied. However, we can see from (1) that the covariance matrix of the input vector z(Mn − τ ) does not have the Toeplitz matrix property such that fast RLS algorithms cannot be developed [18] to solve h i (τ ) based on z(Mn − τ ) and a training sequence.…”

Section: System Model and Polyphase Decompositionmentioning

confidence: 99%

“…However, noise, interference, and channel distortion always exist in a communication system, which draws the need for adaptive reconstruction at the TMUX receiver. In fact, we know that some popular adaptive filtering algorithms, e.g., least mean squares (LMS) [18,19] and recursive least squares (RLS) [7], have been applied for signal reconstruction in a subband system. LMS algorithms are simple in architecture, but slow convergence rate may be a drawback in their applications with a long filter length [18,19].…”

Section: Introductionmentioning

confidence: 99%

“…In fact, we know that some popular adaptive filtering algorithms, e.g., least mean squares (LMS) [18,19] and recursive least squares (RLS) [7], have been applied for signal reconstruction in a subband system. LMS algorithms are simple in architecture, but slow convergence rate may be a drawback in their applications with a long filter length [18,19]. In [18], it was noted that the filterbank system consists of decimation and interpolation operations and thus fast RLS (FRLS) algorithms cannot be applied since the synthesis bank does not have a Toeplitz structure.…”

Section: Introductionmentioning

confidence: 99%

“…LMS algorithms are simple in architecture, but slow convergence rate may be a drawback in their applications with a long filter length [18,19]. In [18], it was noted that the filterbank system consists of decimation and interpolation operations and thus fast RLS (FRLS) algorithms cannot be applied since the synthesis bank does not have a Toeplitz structure.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Stabilized Multichannel Fast RLS Algorithm for Adaptive Transmultiplexer Receivers

Chang

Chiu²

2009

Circuits Syst Signal Process

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The transmultiplexer (TMUX) system has been studied for its application to multicarrier communications. The channel impairments including noise, interference, and distortion draw the need for adaptive reconstruction at the TMUX receiver. Among possible adaptive methods, the recursive least squares (RLS) algorithm is appealing for its good convergence rate and steady state performance. However, higher computational complexity due to the matrix operation is the drawback of utilizing RLS. A fast RLS algorithm used for adaptive signal reconstruction in the TMUX system is developed in this paper. By using the polyphase decomposition method, the adaptive receiver in the TMUX system can be formulated as a multichannel filtering problem, and the fast algorithm is obtained through the block Toeplitz matrix structure of received signals. In addition to the reduction of complexity, simulation results show that the adaptive TMUX receiver has a convergence rate close to that of the standard RLS algorithm and the performance approaches the minimum mean square error solution.

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Section: Resultsmentioning

confidence: 99%

Section: System Model and Polyphase Decompositionmentioning

confidence: 99%