Fast adaptive polynomial I and Q predistorter with global optimisation

“…In addition, their adaptive implementations are slow in convergence. To overcome the nonlinear optimization problem and to have an efficient implementation, besides traditional lookup-table (LUT) techniques [6], many recent researchers focus on the static polynomial model, which is linear in its parameters and hence can significantly reduce the computational burden [7]- [11]. A recent work takes advantage of both LUT and polynomial models to design a joint predistorter [12].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Compensation of High Power Amplifier Distortion for Communication Using a Histogram-Based Method

Huang

Leung

2006

IEEE Trans. Signal Process.

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A probabilistic approach to compensate the nonlinear distortion caused by a high power amplifier (HPA) in a communication system is proposed here. This is a nonparametric method that involves estimating two probabilistic cumulative distribution functions without any explicit parameter estimation as in the conventional compensation techniques. It is shown analytically that the maximum compensation error of the proposed method is bounded and small. An adaptive implementation is developed. Experiments are setup to collect the HPA data. Real data analysis shows that the proposed predistorter has an improved performance compared to the conventional polynomial and neural network predistortion techniques.

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“…Secondly, the signal predistortion technique, which consists of a PD placed between the shaping filter and the HPA. In this case, the objective of the PD is to implement nonlinearity in the third or fifth order; the coefficients are such as to cancel the intrinsic nonlinearity of the HPA up to the terms of the same order [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Predistortion and Postdistortion for Nonlinear Channel

Rodríguez

Soto

Carrasco

1999

NCA

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This paper proposes a new adaptive predistortionpostdistortion scheme based on a recurrent neural network to reduce nonlinear distortion introduced by a high power amplifier in the amplitude and phase of received Quadrature Phase Shift Keying (QPSK) signals in a digital microwave system. The recurrent neural network structure is inspired by the model proposed by Williams and Zipser, with a modified backpropagation algorithm. The input signal is processed by a nonlinear predistorter which reduces the warping effect. The received output signal is passed through a postdistorter to compensate for the warping and clustering effects produced by an amplifier. The proposed scheme yields a significant improvement when it is compared to the system without predistortion-postdistortion, performance is evaluated in terms of the bit error rate and output signal constellation.

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Fast adaptive polynomial I and Q predistorter with global optimisation

Cited by 17 publications

References 6 publications

Analysis and design of an adaptive polynomial predistorter with the loop delay estimator

Analysis and design of an adaptive polynomial predistorter with the loop delay estimator

Nonlinear Compensation of High Power Amplifier Distortion for Communication Using a Histogram-Based Method

Adaptive Predistortion and Postdistortion for Nonlinear Channel

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