Design methods for periodically time varying digital filters

Critchley, J. K.; Rayner, P.J.W.

doi:10.1109/29.1576

Cited by 19 publications

(5 citation statements)

References 13 publications

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“…The proof of property (4) is given in Appendix B; the proof of property (5) is similar. Properties (2), (4), and (5) show that the norm of the system (to bedened in the next section) is unaected by shifts or reversal and conjugation of h; this result is trivial for LTI systems, but is not obvious for multirate systems. Proposition 2.1.…”

Section: Relations Between the Standard And Matrixmentioning

confidence: 98%

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Design of a class of multirate systems using a maximum relative ℒ/sup 2/-error criterion

Campbell

Parks

1997

IEEE Trans. Signal Process.

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A criterion for designing the class of multirate systems for rate-changing is presented. This criterion arises from a model-matching perspective with maximum relative < 2 -error over a general class of inputs and is a natural extension of the standard Chebyshev method for lter design. Using multirate and convex analysis techniques, the criterion is shown to lead to a convex matrix-valued-function approximation problem. An algorithm using convex optimization is proposed to solve the problem. An example illustrates the use of the algorithm and eectiveness over standard lter-design techniques.

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Section: Relations Between the Standard And Matrixmentioning

confidence: 98%

“…We explore a criterion for the design of rate-changing systems. Criteria using a specic function class [1], stochastic inputs [1, 2 , 3 ], and approximating function methods [4,5] have been considered for general multirate systems. If little is known about the class of inputs, a general approach i s preferable.…”

Section: Introductionmentioning

confidence: 99%

Design of a class of multirate systems using a maximum relative ℒ/sup 2/-error criterion

Campbell

Parks

1997

IEEE Trans. Signal Process.

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“…Following from (8) and (21), for a TMUX system E with FIR transmitters and receivers, its IO relation between the TMUX inputs and reconstruction error is given by (E ) :…”

Section: State-space Description Of Tmux Systemsmentioning

confidence: 99%

“…First, as stated before, one problem often encountered in a TMUX system is crosstalk, for example, the crosstalk between multiple services transmitting through the same telephone cable is the primary limitation to digital subscriber line services [21]. Usually, special requirement on system crosstalk performance is imposed, for example, in the British telecommunication specifications, for a 60-channel TMUX, at least 60 dB interchannel crosstalk attenuation is required [8], which is a less strict requirement than crosstalk cancellation, means less cost for implementation.…”

Section: H ∞ Optimization On Crosstalk Attenuationmentioning

confidence: 99%

“…For noise-free TMUX system, a lot of conventional researches have been devoted to exploit the perfect reconstruction property. As such, it has been studied from the point of view of periodically time-varying (PTV) filters in [8,9], with the technique of the selection of PTV filters poles and zeros. In [10], an H 2 optimization approach is used to design nonuniform-band TMUX systems, resulting in Near PR (NPR) TMUX systems.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Design of Noisy Transmultiplexer Systems

Zhou

Xie

2006

EURASIP J. Adv. Signal Process.

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An optimal design method for noisy transmultiplexer systems is presented. For a transmultiplexer system with given transmitters and desired crosstalk attenuation, we address the problem of minimizing the reconstruction error while ensuring that the crosstalk of each band is below a prescribed level. By employing the mixed H 2 /H ∞ optimization, we will ensure that the system with suboptimal reconstruction error is more robust and less sensitive to the changes of input signals and channel noises. Due to the overlapping of adjacent subchannels, crosstalk between adjacent channels is expected. And the problem of crosstalk attenuation is formulated as an H ∞ optimization problem, solved in terms of linear matrix inequalities (LMIs). The simulation examples demonstrate that the proposed design performs better than existing design methods.

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