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.
iterative learning control