RF Power Amplifier Linearization in Professional Mobile Radio Communications Using Artificial Neural Networks

Abstract

This paper is focused on the linearization of the radio frequency power amplifier of a professional digital handheld by means of an artificial neural network. The simplicity of the neural network that is used, together with the fact that a feedback path is unnecessary, make this solution ideal to reduce both the cost of a handheld and its hardware complexity, while fully maintaining its performance. A compensation system is also needed to keep the linearization characteristics of the neural network stable against frequency, temperature and voltage variations. The whole solution that comprises both the neural network and the compensation system has been implemented in the DSP of a real handheld and afterwards fully tested. It has proved to be satisfactory to meet the telecommunication standard requirements in all frequency, temperature and voltage ranges under consideration, while efficient to lower the computational cost of the handheld and to make its internal hardware simpler in comparison with other traditional linearization techniques. The results obtained demonstrate that a neural network can be used to linearize the power amplifiers that are used in transmitters of telecommunication equipment, leading to a significant reduction of both their hardware cost and complexity.

On-Demand Real-Time Optimizable Dynamic Model Sizing for Digital Predistortion of Broadband RF Power Amplifiers

“…In [20], the authors adopted a similar strategy but updated DPD coefficients with a theoretically derived scaling rule instead of optimized parameters. A neural network model was developed in [21] which explicitly measures and models PA characteristics under predetermined frequency, voltage and temperature conditions. In [22], the long-term memory effects of gallium nitride (GaN) PAs are examined and compensated using physics-based models.…”

LDMOS versus GaN RF Power Amplifier Comparison Based on the Computing Complexity Needed to Linearize the Output

“…For this, a multilayer perceptron (MLP) neural network DPD was selected as predistorter technique. This technique has previously shown its advantages compared to classical predistortion techniques [13]. The MLP DPD distorts the PA input signal, providing a correction to the amplifier output nonlinearities (Figure 9), thus improving the ACP of the output signal.…”

“…An MLP consisting of one hidden layer with 20 processors using the hyperbolic tangent output function was trained to provide the predistortion values required to extend the linear range of the power amplifier output [13]. The single processors in input and output layers both provide linear transfer functions.…”

A Tunable Diode-Based Reflective Analog Predistortion Linearizer for Microwave Power Amplifiers

“…Various PA linearization techniques can be classified mainly as feedforward [6,7], feedback [8][9][10][11], and predistortion (PD) in both analog [12][13][14][15][16][17][18][19][20][21] and digital [22][23][24][25] domains. Using neural networks as a method to determine the necessary distortion to be added has also been reported in literatures [25][26][27][28]. Among these methods, analog PD linearizers show promise due to their simplicity, bandwidth, and ability to operate as an adjustable stand-alone unit.…”