Adaptive predistortion for solid state power amplifier using multilayer perceptron

Langlet, F.; Abdulkader, H.; Roviras, Daniel; Maller, A.; Castanié, F.

doi:10.1109/glocom.2001.965132

Cited by 5 publications

(8 citation statements)

References 3 publications

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“…Arguably, the linearization of power amplifier, in either predistortion or postequalization forms, has been the most frequently encountered application that incorporates adaptive nonlinear signal processing using MLP, recurrent, and radial basis function networks in the complex domain. Examples of their applications in communications are Benvenuto et al (1991), Benvenuto and Piazza (1992), Chen, Grant, McLaughlin, and Mulgrew (1993), Kechriotis and Manolakos (1994), Ibnkahla and Castanie (1995), You andHong (1998), Uncini, Vecci, Campolucci, andPiazza (1999), Deng, Sundararajan, and Saratchandran (2000), Langlet, Abdulkader, Roviras, Mallet, and Castanie (2001), Katz (2001), andPark andJeong (2002). In the applications considered in these references, it is critical to combat the often time-varying nonlinear distortion of signal constellation to match a set of desired target complex vectors.…”

Section: Application Of Complex Mlpmentioning

confidence: 97%

Approximation by Fully Complex Multilayer Perceptrons

2003

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We investigate the approximation ability of a multilayer perceptron (MLP) network when it is extended to the complex domain. The main challenge for processing complex data with neural networks has been the lack of bounded and analytic complex nonlinear activation functions in the complex domain, as stated by Liouville's theorem. To avoid the conflict between the boundedness and the analyticity of a nonlinear complex function in the complex domain, a number of ad hoc MLPs that include using two real-valued MLPs, one processing the real part and the other processing the imaginary part, have been traditionally employed. However, since nonanalytic functions do not meet the Cauchy-Riemann conditions, they render themselves into degenerative backpropagation algorithms that compromise the efficiency of nonlinear approximation and learning in the complex vector field. A number of elementary transcendental functions (ETFs) derivable from the entire exponential function e(z) that are analytic are defined as fully complex activation functions and are shown to provide a parsimonious structure for processing data in the complex domain and address most of the shortcomings of the traditional approach. The introduction of ETFs, however, raises a new question in the approximation capability of this fully complex MLP. In this letter, three proofs of the approximation capability of the fully complex MLP are provided based on the characteristics of singularity among ETFs. First, the fully complex MLPs with continuous ETFs over a compact set in the complex vector field are shown to be the universal approximator of any continuous complex mappings. The complex universal approximation theorem extends to bounded measurable ETFs possessing a removable singularity. Finally, it is shown that the output of complex MLPs using ETFs with isolated and essential singularities uniformly converges to any nonlinear mapping in the deleted annulus of singularity nearest to the origin.

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Section: Application Of Complex Mlpmentioning

confidence: 97%

Approximation by Fully Complex Multilayer Perceptrons

2003

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“…To simulate a solid-state power amplifier, the following model [6] is used for the AM/AM conversion:…”

Section: A Power Amplifier Modelmentioning

confidence: 99%

Low-complexity adaptive algorithms for pre-distortion of power amplifiers

Chen

Chiueh

2005

2005 IEEE International Symposium on Circuits and Systems (ISCAS)

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Abstract-Adaptive systems have been broadly used in a wide range of applications and can be categorized into linear or nonlinear adaptive systems. Unfortunately, hardware complexity for implementing adaptive systems is usually high. In this paper, a low-complexity LMS algorithm based on a new number system is proposed and applied to multilayer perceptron (MLP). Simulation results show that the proposed adaptive algorithm, with its much lower complexity, can still achieve comparable performance to traditional adaptive algorithms.

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“…There are several solutions to operate the HPA close to its saturation point without generating non-linearities [2]- [6]. One is to implement a module directly onboard the payload, just before the HPA, in order to obtain a linear transfer characteristic at the HPA output [4]- [6]. This solution, called the predistortion method, has the advantage to correct the HPA non-linearities directly onboard the satellite.…”

Section: Introductionmentioning

confidence: 99%

Predistortion system implementation based on analog Neural Networks for linearizing High Power Amplifiers transfer characteristics

Mulliez¹,

Moutaye²,

Tap³

et al. 2013

2013 36th International Conference on Telecommunications and Signal Processing (TSP)

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In order to correct non-linearities due to High Power Amplifiers (HPA) operating near saturation in telecommunication transceivers, a new adaptive predistortion system based on analog Neural Networks (NNs) was developed. Based on size, consumption and bandwidth considerations, Multi-Layer Perceptron (MLP) type NNs were implemented in a 0.6 µm CMOS ASIC. The NNs parameters are digitally updated with a computer, depending on simulation conditions (temperature drifts, ageing variations). The interface between the analog part and the software updating system is integrated in an analog-digital PCB including a FPGA, 6 analog-to-digital converters and 62 digital-to-analog converters. This paper describes the realization of each part of the breadboard system and presents experimental validation results of the whole predistortion module.

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Adaptive predistortion for solid state power amplifier using multilayer perceptron

Cited by 5 publications

References 3 publications

Approximation by Fully Complex Multilayer Perceptrons

Approximation by Fully Complex Multilayer Perceptrons

Low-complexity adaptive algorithms for pre-distortion of power amplifiers

Predistortion system implementation based on analog Neural Networks for linearizing High Power Amplifiers transfer characteristics

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