Implementation of the transposed Farrow structure

Babić, Djordje; Vesma, J.; Saramäki, T.; Renfors, Markku

doi:10.1109/iscas.2002.1010374

Cited by 40 publications

(18 citation statements)

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“…(1) There are always high ripple values between the image frequencies. Therefore, a uniform stopband would require extremely high values for N and M. (2) The length of the prefilter is 11.…”

Section: Discussionmentioning

confidence: 99%

“…However, it does not offer good anti-aliasing properties for applications where the sampling rate is decreased (decimation). A better solution for decimation is the transposed Farrow structure, where the piecewise polynomial impulse response is determined not for the input but for the output sampling intervals [2], [9], [21]. Therefore, the transposed Farrow structure offers the same attenuation in the aliasing bands as the original structure in the imaging bands if the same polynomial coefficients are used.…”

Section: The Farrow Structurementioning

confidence: 99%

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Polynomial-Based Interpolation Filters—Part I: Filter Synthesis

Vesma

Saramäki

2007

Circuits Syst Signal Process

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This paper introduces a generalized design method for polynomial-based interpolation filters. These filters can be implemented by using a modified Farrow structure, where the fixed finite impulse response (FIR) sub-filters possess either symmetrical or anti-symmetrical impulse responses. In the proposed approach, the piecewise polynomial impulse response of the interpolation filter is optimized directly in the frequency domain using either the minimax or least mean square criterion subject to the given time domain constraints. The length of the impulse response and the degree of the approximating polynomial in polynomial intervals can be arbitrarily selected. The optimization in the frequency domain makes the proposed design scheme more suitable for various digital signal processing applications and enables one to synthesize interpolation filters for arbitrary desired and weighting functions. Most importantly, the interpolation filters can be optimized in a manner similar to that of conventional linear-phase FIR filters.

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Section: Discussionmentioning

confidence: 99%

Section: The Farrow Structurementioning

confidence: 99%

Polynomial-Based Interpolation Filters—Part I: Filter Synthesis

Vesma

Saramäki

2007

Circuits Syst Signal Process

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“…The Farrow structure is efficient for interpolation whereas, for decimation, it is better to use the transposed Farrow structure, as discussed in Babic et al (2002); Hentschel & Fettweis (2000).…”

Section: Sampling Rate Conversion: Farrow Structurementioning

confidence: 99%

“…Generally, S k (z) are designed so that H(z) approximates an allpass transfer function with FD of μ over the frequency range of interest according to Babic et al (2002); Johansson & Hermanowicz (2006); Johansson & Löwenborg (2003); Pun et al (2003);Tseng (2002); Vesma & Saramäki (1996;2000). The desired causal magnitude and unwrapped…”

Section: Sampling Rate Conversion: Farrow Structurementioning

confidence: 99%

Reconfigurable Multirate Systems in Cognitive Radios

Eghbali¹,

Johansson²

2012

Foundation of Cognitive Radio Systems

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“…The Farrow structure is efficient for interpolation whereas, for decimation, it is better to use the transposed Farrow structure [2,17]. These two options are therefore used accordingly in the proposed TMUXs, as seen later in Figs.…”

Section: Farrow Structurementioning

confidence: 99%

A Class of Multimode Transmultiplexers Based on the Farrow Structure

Eghbali

Johansson

Lowenborg

2011

Circuits Syst Signal Process

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This paper introduces multimode transmultiplexers (TMUXs) in which the Farrow structure realizes the polyphase components of general lowpass interpolation/decimation filters. As various lowpass filters are obtained by one set of common Farrow subfilters, only one offline filter design enables us to cover different integer sampling rate conversion (SRC) ratios. A model of general rational SRC is also constructed where the same fixed subfilters perform rational SRC. These two SRC schemes are then used to construct multimode TMUXs. Efficient implementation structures are introduced and different filter design techniques such as minimax and least-squares (LS) are discussed. By means of simulation results, it is shown that the performance of the transmultiplexer (TMUX) depends on the ripples of the filters. With the error vector magnitude (EVM) as the performance metric, the LS method has a superiority over the minimax approach.

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Implementation of the transposed Farrow structure

Cited by 40 publications

References 1 publication

Polynomial-Based Interpolation Filters—Part I: Filter Synthesis

Polynomial-Based Interpolation Filters—Part I: Filter Synthesis

Reconfigurable Multirate Systems in Cognitive Radios

A Class of Multimode Transmultiplexers Based on the Farrow Structure

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