Digital Compensation Techniques for Power Amplifiers in Radio Transmitters
Doctoral thesis, 2017

Power amplifiers (PAs) are vital components in radio transmitters because they are responsible to amplify the low power communication signals to power levels suitable for transmission. Important requirements of PAs are high efficiency and linearity. Unfortunately, there is a tradeoff between efficiency and linearity. In order to satisfy both requirements, designers prefer to prioritize the efficiency in the design process while the linearity is taken care of later using external linearization techniques. Among the linearization techniques proposed in the literature, digital predistortion (DPD) has drawn a large attention of the industrial and academic sectors because it can provide a good compromise between linearity, implementation complexity and efficiency. This thesis treats different aspects related to the compensation of PA nonlinear distortion through DPD. One issue in the synthesis of DPD is that the optimal output from a predistorter is unknown. To overcome this problem, the concept of iterative learning control (ILC) for the linearization of PAs is introduced.  An ILC scheme is derived that is able to identify the optimal predistorted signal that linearizes a PA.  Based on the ILC framework, a novel approach to derive model structures for digital predistorters is proposed. Techniques to identify the parameters of digital predistorters have been developed. Three parameter identification techniques based on ILC have been proposed: an offline technique that can be used for research purposes to select proper models for predistorters, an adaptive technique that is able to achieve better performance than conventional identification techniques used in DPD, and an identification technique that allows us to estimate the predistorter parameter using only one of the in-phase/quadrature (IQ) components of the PA output signal. The issue of gain normalization in ILA has been investigated. A variant to ILA that eliminates the need for a normalization gain and simplifies the DPD synthesis is proposed. Performance limits on PA linearization has also been investigated and an expression for the lower bound for the normalized mean square error (NMSE) performance has been derived. The improved linearity performance achieved through the techniques developed in this thesis can enable a better utilization of the potential performance of existing and emerging highly efficiency PAs, and are therefore expected to have an impact in future wireless communication systems.

nonlinear system


power amplifier

iterative learning control

digital predistortion

Volterra series

Kollektorn, MC2 Building
Opponent: Prof. Anding Zhu


Jessica Chani Cahuana

Chalmers, Signals and Systems, Communication and Antenna Systems, Communication Systems

Iterative Learning Control for RF Power Amplifier Linearization

IEEE Transactions on Microwave Theory and Techniques,; Vol. 64(2016)p. 2778-2789

Journal article

Structured Digital Predistorter Model Derivation Based on Iterative Learning Control

2016 46th European Microwave Conference,; (2016)p. 178-181

Paper in proceedings

A Wideband and Compact GaN MMIC Doherty Amplifier for Microwave Link Applications

IEEE Transactions on Microwave Theory and Techniques,; Vol. 61(2013)p. 922-930

Journal article

A new variant of the indirect learning architecture for the linearization of power amplifiers

European Microwave Week 2015: "Freedom Through Microwaves", EuMW 2015 - Conference Proceedings, 2015 45th European Microwave Conference Proceedings, EuMC,; (2015)p. 1295-1298

Paper in proceedings

On the Behavior of the Normalized Mean Square Error in Power Ampli fier Linearization

Digital predistortion parameter identi fication technique using real-valued measurement output data

Areas of Advance

Information and Communication Technology

Subject Categories


Signal Processing

Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 978-91-7597-596-2


Chalmers University of Technology

Kollektorn, MC2 Building

Opponent: Prof. Anding Zhu

More information


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