Novel Adaptive Nonlinear Predistorters Based on the Direct Learning Algorithm

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Abstract

The adaptive nonlinear predistorter is an effective technique to compensate for the nonlinear distortion existing in digital communication and control systems. However, available adaptive nonlinear predistorters using indirect learning are sensitive to measurement noise and do not perform optimally. Other available types are either slow to converge, complicated in structure and computationally expensive, or do not consider the memory effects in nonlinear systems such as a high power amplifier (HPA). In this paper, we first propose several novel adaptive nonlinear predistorters based on direct learning algorithms - the nonlinear filtered-x RLS (NFXRLS) algorithm, the nonlinear adjoint LMS (NALMS) algorithm, and the nonlinear adjoint RLS (NARLS) algorithm. Using these new learning algorithms, we design adaptive nonlinear predistorters for an HPA with memory effects or for an HPA following a linear system. Because of the direct learning algorithm, these novel adaptive predistorters outperform nonlinear predistorters that are based on the indirect learning method in the sense of normalized mean square error (NMSE), bit error rate (BER), and spectral regrowth. Moreover, our developed adaptive nonlinear predistorters are computationally efficient and/or converge rapidly when compared to other adaptive nonlinear predistorters that use direct learning, and furthermore can be easily implemented. We further simplify our proposed algorithms by exploring the robustness of our proposed algorithm as well as by examining the statistical properties of what we call the "instantaneous equivalent linear" (IEL) filter. Simulation results confirm the effectiveness of our proposed algorithms

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