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In this chapter, the various sources of distortions present in wireless transmitters are presented and the metrics used for characterizing distortions are given.
This chapter discusses the impact of the presence of memory effects on the behavior of nonlinear systems. The different types of memory effects are discussed and models to describe systems with memory are covered.
This chapter covers the evaluation of the performance of behavioral models. Various metrics used for this purpose are studied. Additionally, the effect of the presence of memory on the accuracy of the metrics is investigated and techniques for the cancelation of static nonlinearities are presented.
This chapter is concerned with discussing quasi‐memoryless behavioral models. Various classes of models used for describing the behavior of such systems are presented, ranging from look‐up table (LUT) models to polynomial models utilizing advanced basis functions with special properties.
In this chapter, the popular memory polynomial model and its variants are discussed. The various models discussed are compared in terms of performance and the trade‐off between computational complexity and accuracy is presented.
This chapter discusses box‐oriented models used for predistortion applications, and explains their advantages in reducing the number of coefficients required and improving the conditioning of model matrices.
In this chapter, the use of neural network based models for characterizing and compensating power amplifier nonlinearities is examined. Different topologies of neural networks models and their training algorithms are presented.
This chapter is concerned with the behavioral modeling procedure and mainly the effects of the stimuli used for the DUT characterization. The various types of test signals and methods of characterizing DUTs are discussed, and major identification techniques used to identify the models coefficients are covered.
In this chapter, advanced topics in predistortion are discussed such as linearizing transmitters exhibiting RF front end imperfections (such as gain and phase imbalance) in addition to the power amplifier nonlinearity. Also, multi‐band and MIMO predistortion techniques are covered.
This chapter discusses the implementation of predistorters in the baseband. Key issues related to the application of digital predistortion technique such as power alignment, direct and indirect learning techniques, and open and closed loop architectures are covered. Also, the concept of power and bandwidth scalable digital predistorters is introduced.
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