A frequency sampling filter design method which accounts for finite word length effects

Stubberud, Peter; Leondes, Cornelius T.

doi:10.1109/78.258134

Cited by 11 publications

(5 citation statements)

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“…Derived as above, the method presented in this paper is independent of the amplitude mismatch. Methods for correcting the amplitude mismatch are already known in the art, where the Frequency Sampling Method (FSM) is an efficient technique [29,30]. FSM estimates the amplitude-frequency response of the filter based on its sampled complex frequency response and the corresponding Inverse Discrete Fourier transform (IDFT).…”

Section: Hardware Platform Experiments and Resultsmentioning

confidence: 99%

“…For the simple presentation of the LPI analysis, we apply FSM to design the compensation filter and compensate for the amplitude-frequency response mismatch between the I/Q channel, which is not detailed because it is not the focus of the paper. The design details can be found in [30]. Therefore, it should be noted that the experiment on the platform is based on the premise of no amplitude mismatch.…”

Section: Hardware Platform Experiments and Resultsmentioning

confidence: 99%

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I/Q Linear Phase Imbalance Estimation Technique of the Wideband Zero-IF Receiver

Meng

Wang

et al. 2020

Electronics

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The in-phase/quadrature (I/Q) imbalance encountered in the zero-IF receiver leads to incomplete image frequency suppression, which severely deteriorates image rejection ratio (IRR) of the receiver system and must be improved using additional analog or digital signal processing. The I/Q linear phase imbalance (LPI) is the key of the I/Q imbalance, which consists of the time delay deviation (TDD) and the local oscillator (LO) phase offset. TDD is negligible in most literature, but it degrades system performance largely for wideband communication systems. This paper proposes a method based on the cross-power spectrum between the I/Q signal to address the estimation problem of LPI. Compared with other conventional methods, the proposed approach calculates LPI parameters simultaneously without any additional hardware. The MATLAB simulation is utilized to evaluate the effectiveness of the presented method. Moreover, the experimental platform of detailed design demonstrates the feasibility of the proposed estimation method, and IRR of the system before and after compensation shows that LPI has been accurately estimated and eliminated with the help of an appropriate compensation structure. Both reveal that the proposed method offers an effective solution to the LPI problem.

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Section: Hardware Platform Experiments and Resultsmentioning

confidence: 99%

Section: Hardware Platform Experiments and Resultsmentioning

confidence: 99%

I/Q Linear Phase Imbalance Estimation Technique of the Wideband Zero-IF Receiver

Meng

Wang

et al. 2020

Electronics

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“…Using an uniform sampled set F x , the Frequency Sampling Method (FSM) is a well known and efficient technique [31], [32] that is able to estimate the impulse response of a filter from its sampled complex frequency response. The FIR filter h IQ (n) = h IQ,r (n) + j h IQ,i (n) is evaluated according to the following FSM steps: i) the Inverse Fast Fourier transform (IFFT) of vector H IQ is computed at first; ii) the resulting vector is then truncated to length R, iii) final windowing and symmetrization is performed to retain causality of h IQ (n).…”

Section: A Complete Model (Cm)mentioning

confidence: 99%

“…The total length of both real FIR filters h IQ,r (n) and h IQ,i (n) is L = 2R + 1 samples, while the selection of R depends on the trade-off between the degree of I/Q imbalance of the device under test and system complexity of the compensation filters. For details about the FSM method, considerations about finite length arithmetic, number and positions of tone frequencies, the interested reader can take a look at [32]. Finally, the proposed measurement method does not suffer from gain…”

Section: A Complete Model (Cm)mentioning

confidence: 99%

I/Q Compensation of Broadband Direct-Conversion Transmitters

Rampa¹

2014

IEEE Trans. Wireless Commun.

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The aim of this paper is to propose a compensation strategy that is able to easily identify the model of the Inphase/Quadrature (I/Q) impairments of Radio-Frequency (RF) direct-conversion devices and to efficiently compensate these unwanted effects. In fact, direct-conversion transmitters that integrate analog and digital components introduce a wideband frequency-dependent I/Q mismatch that strongly reduces the upconverter performances. The wider the signal bandwidth or the higher its spectral efficiency, the more severe the I/Q artifacts on the upconverted signal become. The proposed compensation strategy can mitigate these unwanted effects, eliminating I/Q impairments with a very simple hardware architecture. The compensation model adopted here for a generic upconverter device does not assume any hypotheses about signal modulation and system architecture and can be easily adapted to accommodate specific hardware systems or different wireless standards. To show the capabilities of the proposed compensation scheme, an experimental setup has been arranged to emulate system configurations found in direct-conversion RF Integrated Circuits (RFIC) for 3.5/4G applications.

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“…Research in the signal-processing field is looking at designing modified frequency-sampling filters that account for finite word-length effects [11]. In reference [12], the authors have combined this model structure with a standard least-squares estimator in order to directly obtain an estimate of the process frequency response with a frequency-domain statistical bound on the model uncertainty.…”

Section: Modelling Ofmentioning

confidence: 99%

MIMO Frequency Sampling Filters for Modelling of MIMO System Applications

Aziz

Mohd‐Mokhtar

2013

j.eng.technol.sci.

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Abstract. In the modelling of a system based on approach, data acquisition is the first procedure that must be data acquisition process from data. This may lead to unacceptable computational time during the identification process. Raw data may also suffer severe noise disturbance high frequency region. In addition, bias estimatio considers direct identification from a closed problem, in this paper a introduced. Multi-input obtain only relevant model of the analyzed data. By performing this procedure, compressed, cleaned and unbiased data are produced. The efficacy of the MIMO frequency sampling filters was demonstrated by process data and steam generator plant data. raw data was successfully compressed and identification purposes with less computational time used to develop a model of the system, analysis, and also for developing a new Keywords: Data compression parametric model; system identification. IntroductionSystem identification is based on study and analysis of input and output data collected from a system. modelling of a system based on a system identification approach, data acquisition is the first procedure that must be carried out.acquisition process from a real system typically yields large amounts of This may lead to unacceptable computational time during the identification process. Raw data may also suffer severe noise disturbance, especially in high frequency region. In addition, bias estimation will occur if one only considers direct identification from a closed-loop system. To overcome in this paper a multivariable frequency sampling filter approach is input-multi-output (MIMO) raw data are analyzed in order to and meaningful parameters that describe the empirical model of the analyzed data. By performing this procedure, compressed, cleaned and unbiased data are produced. The efficacy of the MIMO frequency sampling demonstrated by compressing two sets of data: pH neutralization process data and steam generator plant data. The results show that the amount of successfully compressed and that the output was ready for identification purposes with less computational time, i.e. they could be further used to develop a model of the system, to conduct time and frequency response developing a new control system design.compression; frequency sampling filters; multivariable process system identification.System identification is based on study and analysis of input and output data system. To perform this procedure, a data acquisition process is unavoidable. Data acquisition from a real system typically yields large amounts time data. This may lead to unacceptable computational time during the identification process. Also, the raw data may contain complex system an extended version of the paper published by the authors in Int. Conf. Modelling ofElectronic Engineering, Universiti Sains Malaysia, School of Electrical & Electronic Engineering, Universiti Sains Malaysia, system identification carried out. The real system typically yields large amounts of This may lead to unacceptable computational time during ...

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A frequency sampling filter design method which accounts for finite word length effects

Cited by 11 publications

References 11 publications

I/Q Linear Phase Imbalance Estimation Technique of the Wideband Zero-IF Receiver

I/Q Linear Phase Imbalance Estimation Technique of the Wideband Zero-IF Receiver

I/Q Compensation of Broadband Direct-Conversion Transmitters

MIMO Frequency Sampling Filters for Modelling of MIMO System Applications

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