Selection of a Time-Varying Quadratic Volterra Model Using a Wavelet Packet Basis Expansion

Green, M.; Zoubir, Abdelhak M.

doi:10.1109/tsp.2004.834411

Cited by 8 publications

(5 citation statements)

References 31 publications

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“…The LPE Volterra model of (4) has been used extensively in RF research. The model has been applied to develop solutions related to nonlinear communication system modeling and estimation [12], satellite communication [13], digital transmission channel equalization [14], multichannel nonlinear CDMA system equalization [15], analysis, and cancellation of the intercarrier interference in nonlinear orthogonal frequency division multiplexing (OFDM) systems [16], decision feedback equalization [17], nonlinear system and circuit analysis [18], data predistortion [19], PA modeling [20]- [22], and DPD [23]- [25]. Although computationally more efficient than its passband counterpart, the LPE Volterra series in its classical form (4) still suffers from a large number of kernels.…”

Section: Classical Lpe Volterra-series Overviewmentioning

confidence: 99%

“…The model is then given by (20) Using the fading memory assumption for the steady-state response of the PA (the transient-response time-invariant Volterra series is defined as ) [27], the corresponding model can be represented as (21) Using the symmetry of the integrated function (distortion components are symmetrical and Volterra kernels can be symmetrized [11]), the model can be simplified to (22) Digitization of the model yields (23) where and denote the memory depth of the first-and third-order Volterra series. Similar to the above derivation, it could be shown that the fifth-order Volterra kernel, , is given by (24) where denotes the memory depth of the fifth-order Volterra series.…”

Section: A Model Derivationmentioning

confidence: 99%

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Baseband Equivalent Volterra Series for Behavioral Modeling and Digital Predistortion of Power Amplifiers Driven With Wideband Carrier Aggregated Signals

Fehri

Boumaiza

2014

IEEE Trans. Microwave Theory Techn.

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This paper presents a new baseband equivalent (BBE) Volterra-series model suitable for the behavioral modeling and linearization of wideband RF power amplifiers (PAs). Starting with passband Volterra series, and following a number of signal transformations, a discrete expression relating the envelopes of the output and input signals to the carrier frequency was derived. The new BBE Volterra series reduces the number of kernels, and hence, avoids resorting to the pruning approaches commonly applied in the literature to the classical low-pass equivalent (LPE) Volterra formulation. The proposed baseband Volterra series has similar modeling and linearization performance to the full classical LPE Volterra series. It also outperforms the pruned LPE Volterra models, which use a dynamic deviation reduction approach, in terms of both linearization performance and complexity. The proposed BBE Volterra series was successfully used to linearize different PAs (200-W LDMOS Doherty and 45-W broadband GaN PA) driven with wideband and intra-band carrier aggregated signals (mixed LTE and WCDMA signals).

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Section: Classical Lpe Volterra-series Overviewmentioning

confidence: 99%

Section: A Model Derivationmentioning

confidence: 99%

Baseband Equivalent Volterra Series for Behavioral Modeling and Digital Predistortion of Power Amplifiers Driven With Wideband Carrier Aggregated Signals

Fehri

Boumaiza

2014

IEEE Trans. Microwave Theory Techn.

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“…Nichols applied the Markov chain Monte Carlo for sampling the parameter distributions and extended this approach for identifying the delamination in a composite beam . Green presented a data annealing‐based Markov Chain Monte Carlo (MCMC) algorithm for Bayesian identification of a nonlinear system . In addition, dynamic fuzzy wavelet neural network can be employed to predict the structural responses for damage identification .…”

Section: Introductionmentioning

confidence: 99%

Transfer function‐based Bayesian damage detection under seismic excitation

Vahedi

Khoshnoudian

Hsu

et al. 2019

Structural Design Tall Build

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Summary Transfer function (TF) data are recognized as diagnostic features in damage detection procedure. The objective of this paper is to present a damage detection method in Bayesian paradigm based on TF data due to ground excitation. The measured seismic responses of the structure in the frequency domain are adopted to obtain displacement TFs and the structural natural frequencies are identified from observed TFs. The derived features are utilized for Bayesian structural damage detection. In addition, the challenging issue of underlying flexible soil in real cases has been addressed. The proposed technique is applied to a numerical shear frame to evaluate the capability of the method. An experimental study on a six‐story steel building has been validated to demonstrate the capability of the method for damage detection purpose. The results of studied cases indicated that the proposed method is capable of identifying the location and the severity of damage precisely.

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“…Another weakness of the approaches currently used is that they refer to differential systems, thus being unsuitable for modelling systems described by integral equations. Besides, although some attempts have been done in order to generalize the techniques mentioned above to the case of nonstationary systems [9], Non-Linear Non-Stationary System Identification (NLNSSI) can still be considered as an open problem.…”

Section: Introductionmentioning

confidence: 99%

A computational intelligence technique for the identification of non-linear non-stationary systems

Turchetti

Gianfelici

Biagetti

et al. 2008

2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)

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This paper addresses nonlinear nonstationary system identification from stimulus-response data, a problem concerning a large variety of applications, in dynamic control as well as in signal processing, communications, physiological system modelling and so on. Among the different methods suggested in the vast literature for nonlinear system modelling, the ones based on the Volterra series and the Neural Networks are the most commonly used. However, a strong limitation for the applicability of these methods lies in the necessary property of stationarity, an assumption that cannot be considered as valid in general and strongly affecting the validity of results. Another weakness of the approaches currently used is that they refer to differential systems, thus being unsuitable to model systems described by integral equations. A computational intelligence technique that exploits the potentialities of both the Karhunen-Loève Transform (KLT) and Neural Networks (NNs) representation and without any of the limitations of the previous approaches is suggested in this paper. The technique is suitable for modelling the wide class of systems described by nonlinear nonstationary functionals, thus including both differential and integral systems. It takes advantage of the KLT separable kernel representation that is able to separate the dynamic and static behaviours of the system as two distinct components, and the approximation property of NNs for the identification of the nonlinear no-memory component. To validate the suggested technique comparisons with experimental results on both nonlinear nonstationary differential and integral systems are reported.

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Selection of a Time-Varying Quadratic Volterra Model Using a Wavelet Packet Basis Expansion

Cited by 8 publications

References 31 publications

Baseband Equivalent Volterra Series for Behavioral Modeling and Digital Predistortion of Power Amplifiers Driven With Wideband Carrier Aggregated Signals

Baseband Equivalent Volterra Series for Behavioral Modeling and Digital Predistortion of Power Amplifiers Driven With Wideband Carrier Aggregated Signals

Transfer function‐based Bayesian damage detection under seismic excitation

A computational intelligence technique for the identification of non-linear non-stationary systems

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