Adaptive pre-distortion techniques based on orthogonal polynomials

Dallinger, Robert; Ruotsalainen, Henri; Wichman, Risto; Rupp, Markus

doi:10.1109/acssc.2010.5757879

Cited by 28 publications

(20 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…IMPROVED ADAPTIVE GRADIENT IDENTIFICATION OF THE WIENER MODEL The properties and limitations of identification algorithms 2 for Wiener models has been studied thoroughly in literature [12]- [14]. One inherent drawback of the update equations (5) and (6) is that the time-varying non-linearityĥ k appears in the feedback path of the adaptation of the linear filterĝ k . If both adaptations are running simultaneously, stability (in a wide sense) can barely be ensured.…”

Section: System Modelmentioning

confidence: 99%

See 1 more Smart Citation

Improved robustness and accelerated power amplifier identification with adaptive Wiener models in the complex domain

Dallinger

Rupp

2012

2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)

Self Cite

View full text Add to dashboard Cite

Identification of microwave power amplifiers including their memory effects, can be done computationally inexpensive with adaptive Wiener models which consist of a transversal filter followed by a static non-linearity that is parametrised using a polynomial basis. For identification of both blocks by simultaneously running gradient type algorithms, the resulting combined adaptive scheme suffers from low learning speed and vulnerability to instability. If the identification of the two blocks is done one after the other, the stability region is well defined but the achievable error does not even get near the minimum mean square error (MMSE). As a remedy, we propose a repetition of such a consecutive identification. By this, the algorithm allows to reach the MMSE, while stability is preserved. Based on recent results found for the real-valued case, we motivate the use of specific time-variant step-sizes enabling robust and fast simultaneous adaptation in the complex domain. By Monte Carlo simulations, we illustrate the improved performance that is obtained by the proposed algorithms.

show abstract

Section: System Modelmentioning

confidence: 99%

“…Nevertheless, the Wiener model still allows to resemble memory effects and non-linear behaviour. It consists of a finite impulse response (FIR) filter followed by a static non-linearity [4], [5]. Besides the model itself, also the method for parameter identification needs to be kept as simple as possible.…”

Section: Introductionmentioning

confidence: 99%

Improved robustness and accelerated power amplifier identification with adaptive Wiener models in the complex domain

Dallinger

Rupp

2012

2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Then in the discrete time case, the optimal PD described by the operator {} [1], it is illustrated how the signal distortion introduced by a memory less PA can be approximated by two continuous functions which are closely related (2) and (3), we obtain the approximations of the AM/AM and AM/PM conversions by using the LWNN such that…”

Section: Pa Modelmentioning

confidence: 99%

“…Recently, a lot of efforts have been made to improve the linearity of the PA [1]- [3]. Among all linearization techniques, digital baseband pre-distortion is one of the most cost effective.…”

Section: Introductionmentioning

confidence: 99%

Legendre Wavelet Neural Networks for Power Amplifier Linearization

Zheng¹,

Wei²,

Xu³

2014

View full text Add to dashboard Cite

In this paper, a novel technique for power amplifier (PA) linearization is presented. The Legendre wavelet neural networks (LWNN) is first utilized to model PA and inverse structure of the PA by applying practical transmission signals and the gradient descent algorithm is applied to estimate the coefficients of the LWNN. Secondly, this technique is implemented to identify and optimize the coefficient parameters of the proposed pre-distorter (PD), i.e., the inversion model of the PA. The proposed method is most efficient and the pre-distorter shows stability and effectiveness because of the rich properties of the LWNN. A quite significant improvement in linearity is achieved based on the measured data of the PA characteristics and out power spectrum has been compared.

show abstract

“…We also have used our simulators for generating LTE signals that are required to include realistic signals in related research [8], or as a reference for LTE-compliant measurements. In such cases, the simulator can serve not only as a data pump, but also as a vehicle to evaluate the received data.…”

Section: Introductionmentioning

confidence: 99%

The Vienna LTE simulators - Enabling reproducibility in wireless communications research

Mehlführer¹,

Ikuno²,

Šimko³

et al. 2011

EURASIP J. Adv. Signal Process.

Self Cite

247

136

View full text Add to dashboard Cite

In this article, we introduce MATLAB-based link and system level simulation environments for UMTS Long-Term Evolution (LTE). The source codes of both simulators are available under an academic non-commercial use license, allowing researchers full access to standard-compliant simulation environments. Owing to the open source availability, the simulators enable reproducible research in wireless communications and comparison of novel algorithms. In this study, we explain how link and system level simulations are connected and show how the link level simulator serves as a reference to design the system level simulator. We compare the accuracy of the PHY modeling at system level by means of simulations performed both with bit-accurate link level simulations and PHY-model-based system level simulations. We highlight some of the currently most interesting research questions for LTE, and explain by some research examples how our simulators can be applied.

show abstract

Adaptive pre-distortion techniques based on orthogonal polynomials

Cited by 28 publications

References 28 publications

Improved robustness and accelerated power amplifier identification with adaptive Wiener models in the complex domain

Improved robustness and accelerated power amplifier identification with adaptive Wiener models in the complex domain

Legendre Wavelet Neural Networks for Power Amplifier Linearization

The Vienna LTE simulators - Enabling reproducibility in wireless communications research

Contact Info

Product

Resources

About