Volterra series identification and its applications in structural identification of nonlinear block-oriented systems

Wang, Y. R.; Cheng, Changming

doi:10.1080/00207721.2020.1781289

Cited by 9 publications

(3 citation statements)

References 41 publications

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“…The modeling systems extract pruning methods from VoS to interpolate basis functions in a block-oriented structures to avoid large coefficient assessment; see [89]- [91] for the overviews, which can be classified in different variants as are discussed in the succinct subsections.…”

Section: Representation Of Machine Learning Based Behavioral Modelsmentioning

confidence: 99%

Predistortion-Based Linearization for 5G and Beyond Millimeter-Wave Transceiver Systems: A Comprehensive Survey

Haider

You

et al. 2022

IEEE Commun. Surv. Tutorials

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The next-generation (5G/6G) wireless communication aims to leapfrog the currently occupied sub-6 GHz spectrum to the wideband millimeter-wave (MMW) spectrum. However, MMW spectrums with high-order modulation schemes drive the power amplifier (PA) at significant back-off, causing severe nonlinear distortions, thus deteriorating the transceiver's (TRXs) modulation process. Typically, the TRX efficacy is quantified with standardized linearization matrices, which take advantage of different predistortion (PD) schemes to handle deep compression of the PA. In this regard, TRX baseband signals are mostly linearized in the digital domain, where digitally controlled linearization needs higher sampling rates due to increasing MMW bandwidths to compensate for intermodulation (IMD) products, resulting in increased system cost and power consumption. Alternately, the digitally controlled analog-based linearization, i.e., the hybridization of digital predistortion (DPD) and analog predistortion (APD), is highly productive and cost-effective for fulfilling the linearized energy-efficient design vision of MMW networks. Therefore, this paper puts an extensive spotlight on the progress in PD-based linearization for 5G and beyond communications. It first provides background information on the advancements of PD schemes through recent surveys, then classifies the general roadmap of PD waveform processing across the TRX system models as preliminary. After this, we present three prominent PD architectures and their design approaches with intrinsic performance metrics. Finally, we explore four case studies encompassing PD operation under certain nonlinear constraints of different communication schemes. We examine the suitability of PD-based linearization solutions, both existing and proposed till the first quarter of 2022, and identify the potential prospects in this domain.

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Section: Representation Of Machine Learning Based Behavioral Modelsmentioning

confidence: 99%

Predistortion-Based Linearization for 5G and Beyond Millimeter-Wave Transceiver Systems: A Comprehensive Survey

Haider

You

et al. 2022

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

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“…Volterra series is a powerful mathematical tool for analyzing nonlinear systems [7,8]. In essence, It directly extends the description of the impulse response function of a linear system to a nonlinear system [9]. Volterra series offers a highly structured analysis platform for modeling the nonlinear systems, but the key issue utilizing the Volterra series is the identification of its kernel functions, which has demonstrated to be a great challenge [10].…”

Section: Introductionmentioning

confidence: 99%

“…Artificial neural networks, as a non-parametric modeling method, have been applied vastly since it can approximate any nonlinear function with arbitrary accuracy in theory [9,15]. Neural networks have good approximation capabilities and adaptive features and can also achieve parsimonious modeling [16,17].…”

Section: Introductionmentioning

confidence: 99%

Deterministic and Stochastic Modeling Approaches for Saturation Nonlinearity

Peng

Huang

2022

J. Phys.: Conf. Ser.

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Being different from the most traditional methods of nonlinearity modelling, non-parametric modelling approaches viz. deterministic and stochastic approaches for saturation nonlinearity are proposed in the sense of system impulse response. Based on deterministic approach, the closed-loop system involved saturation nonlinearity can be modelled as the response of the closed-loop system that is subject to impulse stimulation. Alternatively, the closed-loop system involved saturation nonlinearity can be modelled in stochastic manner, in which the impulse response coefficients are estimated by the FCOR algorithm. Moreover, it shows some linear relationship of the impulse response coefficients between different saturation ratios in both the deterministic and stochastic models. This is illustrated by three different numerical examples.

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A Novel Wiener‐Type Dynamic Neural Network Method for Large Signal Modeling of Power Amplifiers

Liu,

Su,

Wang

et al. 2024

Circuit Theory & Apps

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A novel Wiener‐type dynamic neural network (Wiener‐type DNN) method for large signal modeling of power amplifiers (PAs) is proposed in this paper. In this method, the proposed model structure for the PA contains two Wiener‐type DNNs to describe the input impedance and the amplification efficiency, respectively. The Wiener‐type DNN includes a simplified linear dynamic equation module and a nonlinear static equation module. For the simplification process of the linear dynamic equation, it is proposed to process fundamental frequency components of the large signal harmonic data of the PA by vector fitting. The neural network is used to implement the static equation in the Wiener‐type DNN. The formulas for training the proposed Wiener‐type DNN model of the PA using large signal data are derived. The training algorithm of the PA model is proposed to improve the training efficiency. A Motorola MOSFET PA example and a Freescale lateral double‐diffused MOSFET PA example are present to validate the proposed Wiener‐type DNN method. For the Motorola MOSFET PA, a dynamic neural network (DNN) model and a time‐delay deep neural network (TDDNN) are established for comparison. For the Freescale PA, the DNN model is used for comparison experiment. The comparison figures show that the Wiener‐type DNN model has better convergence properties than the DNN model in the nonlinear region of the PA. The established accurate PA model is conducive to the design of subsequent communication circuits.

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Volterra series identification and its applications in structural identification of nonlinear block-oriented systems

Cited by 9 publications

References 41 publications

Predistortion-Based Linearization for 5G and Beyond Millimeter-Wave Transceiver Systems: A Comprehensive Survey

Predistortion-Based Linearization for 5G and Beyond Millimeter-Wave Transceiver Systems: A Comprehensive Survey

Deterministic and Stochastic Modeling Approaches for Saturation Nonlinearity

A Novel Wiener‐Type Dynamic Neural Network Method for Large Signal Modeling of Power Amplifiers

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