Adaptive predistortion lineariser using polynomial functions

Ghaderi, Mohammad Ali; Kumar, S.; Dodds, D.E.

doi:10.1049/ip-com:19941048

Cited by 25 publications

(8 citation statements)

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“…Many techniques have been proposed and studied in the literature to mitigate these nonlinearities, from which adaptive filtering algorithms have emerged as an attractive choice due to their ease of implementation and reasonable performance [2--4]. As a result, a large number of algorithms and variants have been proposed and implemented to solve the dual problems of nonlinear power amplifier and wireless transmitterbehavioral modeling and predistortion [5][6][7]. Due to the large variety of adaptive algorithms available in the literature ( [8]- [9]), it is essential to provide radio frequency (RF) researchers with efficient guidelines to adequately select the appropriate algorithm to be used.…”

Section: Introductionmentioning

confidence: 99%

Behavioral modeling and predistortion of nonlinear power amplifiers based on adaptive filtering techniques

Zerguine

Hammi

Abdelhafiz

et al. 2014

2014 IEEE 11th International Multi-Conference on Systems, Signals &Amp; Devices (SSD14)

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In this paper, the use of some of the most popular adaptive filtering algorithms for the purpose of linearizing power amplifiers by the well-known digital predistortion (DPD) technique is investigated. First, an introduction to the problem of power amplifier linearization is given, followed by a discussion of the model used for this purpose. Next, a variety of adaptive algorithms are used to construct the digital predistorter function for a highly nonlinear power amplifier and their performance is comparatively analyzed. Based on the simulations presented in this paper, conclusions regarding the choice of algorithm are derived.IndexTerms-Adaptive filtering, power amplifier nonlinearities, behavioral modeling, nonlinear system identification.

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Section: Introductionmentioning

confidence: 99%

Behavioral modeling and predistortion of nonlinear power amplifiers based on adaptive filtering techniques

Zerguine

Hammi

Abdelhafiz

et al. 2014

2014 IEEE 11th International Multi-Conference on Systems, Signals &Amp; Devices (SSD14)

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“…Several adaptive complex polynomial predistorters were proposed [19][20][21][22] with the main objectives to increase the convergence rate and to reduce the complexity. In Reference [20], the predistorter polynomial coefficients are derived for minimum out-of-band inter-modulation distortion (IMD).…”

Section: Introductionmentioning

confidence: 99%

“…The third, fifth, and possibly seventh order components can be suppressed, thus the optimization process does not guarantee the convergence to the global minimum. In References [21,22], the authors proposed to adapt the predistorter coefficients by using a postdistorter with an RLS algorithm. The presented simulation results promise a distortion suppression of 45 dB.…”

Section: Introductionmentioning

confidence: 99%

Adaptive polynomial predistorters for M‐QAM transmission using non‐linear power amplifiers

Vo¹,

Le‐Ngoc²,

Besbes³

2006

Wireless Communications

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In this paper, adaptive baseband polynomial predistortion techniques are introduced to counter-balance the AM/AM and AM/PM non-linear effects of the transmit power amplifier. The proposed polynomial predistortion scheme is based on polar coordinate representation. Both LMS and RLS concepts are used to derive the adaptive algorithms. An enhanced LMS-based algorithm with fast convergence and low complexity is proposed. For very fast convergence, a cascaded RLS-based adaptive polynomial predistorter structure is introduced. The performance of the proposed schemes in terms of intermodulation distortion, spectral regrowth, and convergence rate are examined. The obtained results show that the polynomial predistortion schemes can be used in M-QAM transmitters with power amplifiers operating near saturation to achieve a highest power efficiency.

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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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Adaptive predistortion lineariser using polynomial functions

Cited by 25 publications

References 0 publications

Behavioral modeling and predistortion of nonlinear power amplifiers based on adaptive filtering techniques

Behavioral modeling and predistortion of nonlinear power amplifiers based on adaptive filtering techniques

Adaptive polynomial predistorters for M‐QAM transmission using non‐linear power amplifiers

Adaptive Predistortion and Postdistortion for Nonlinear Channel

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