RF Power Amplifier Digital Pre-Distortion (DPD) & Behavioral Modeling
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This 3-day course describes the motivations and methods behind the modeling of RF power amplifiers used for cellular wireless applications (primarily), and the use of these modeling techniques for linearization of RF PAs using digital predistortion techniques. The goals of RF power amplifier design are high efficiency and linearity. With modern cellular communications modulation formats such as LTE and WCDMA these goals are difficult to achieve simultaneously with traditional RF PAs, and high-efficiency architectures such as Doherty, Envelope Tracking, and so forth are becoming more commonplace. These PAs require an additional linearization system to achieve the mandated linearity specifications. The emergence of high-speed digital signal processing techniques has enabled the linearization to be accomplished in the digital signal domain, and digital pre-distortion (DPD) is now the preferred linearization technique.
This course explains the nonlinear behaviour of RF power amplifiers, developing general modeling techniques to describe the nonlinearities and memory effects. A system-level approach to the modeling and linearization of the PA is adopted, and techniques for implementation of DPD in practical situations are described.
Upon completing the course you will be able to:
- Understand and describe the nonlinear behaviour and memory effects found in RF power amplifiers
- Use and understand the mathematical algorithms for behavioural modeling and digital pre-distortion
- Use and implement digital pre-distortion methods for linearization of RF Pas
- Evaluate and compare modeling and DPD techniques
This course is suitable for RF PA designers, DSP designers, and System-Level engineers who are involved in the specification, design, and implementation of linearized RF PAs and transmitter systems, or who are developing pre-distortion methods, software and algorithms for linearization of RF PAs.
Electrical Engineering degree or equivalent and at least two years applicable practical experience is recommended.