Steady-State Performance Analysis and Step-Size Selection for LMS-Adaptive Wideband Feedforward Power Amplifier Linearizer

Abstract

Balancing between power amplifier (PA) linearity and power efficiency is one of the biggest implementation challenges in radio communication transmitters. Among various linearization methods, the feedforward linearization technique is a fairly established principle offering a good tradeoff between linearity and power-efficiency even under wideband operation. Moreover, adaptive techniques for such linearizer have been proposed in literature to track parameter changes in the main PA and other circuitry. Among those, least mean squares (LMS) method for adapting signal cancellation loop (SCL) and error cancellation loop (ECL) coefficients is an attractive low-complexity alternative. In this paper, we carry out extensive closed-form performance analysis of the achievable intermodulation distortion (IMD) reduction of the overall LMS-adaptive feedforward linearizer, as a function of the used step-sizes and essential waveform statistics. Such analysis is currently missing from the state-of-the-art literature. Both memoryless nonlinearities and Wiener-Hammerstein type PA memory models are studied for which IMD suppression expressions are derived. Comprehensive computer simulations are also provided to illustrate the accuracy of the analysis when practical OFDM waveforms are used. Design examples are given as well where the analysis results are used to choose proper linearizer step-sizes to meet given transmitter spectral mask specifications. Index Terms-Feedforward linearizer, intermodulation distortion (IMD), LMS, power amplifier (PA), step-size, Wiener-Hammerstein.

Linearization as a Solution for Power Amplifier Imperfections: A Review of Methods

“…One of the shortcomings of the analysis is that it ignores the memory effects of the amplifier. The least mean squares adaptation estimating the amplifier memory effects using the Wiener-Hammerstein memory model has been thoroughly investigated in [12]. Both analyses propose methods to alleviate the component tolerance requirements for the feedforward circuit.…”

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

“…In this method, the nonlinearity of a power-efficient PA is compensated by additional circuits and signal processing, resulting in a linear and efficient system as a whole. 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].…”

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

“…In order to increase efficiency and to meet the telecommunications standards, many different techniques have been proposed and applied to extend the linear range of the power amplifier response: Cartesian Feedback [1], Feedforward [2], Volterra series [3], Memory Polynomials [4], Wiener and Hammerstein models [5], Lookup Tables (LUT) [6], Artificial Neural Networks (ANN) [7]- [13], Neural-Fuzzy systems [14], Genetic Algorithms [15], etc.…”

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