Iterative Learning Control for RF Power Amplifier Linearization

This paper proposes a new technique to identify the parameters of a digital predistorter based on iterative learning control (ILC). ILC is a well-established control theory technique that can obtain the inverse of a system. Instead of focusing on identifying the predistorter parameters, the technique proposed here first uses an iterative learning algorithm to identify the optimal power amplifier (PA) input signal that drives the PA to the desired linear output response. Once the optimal PA input signal is identified, the parameters of the predistorter are estimated using standard modeling approaches, e.g., least squares. To this end, in this paper, we present a complete derivation of an ILC scheme suitable for the linearization of PAs, which includes convergence conditions and the derivation of two learning algorithms. The proposed ILC scheme and parameter identification technique were demonstrated experimentally and compared with the indirect learning architecture (ILA) and direct learning architecture (DLA). The experimental results show that, even for the most difficult cases, the proposed ILC scheme can successfully linearize the PA. The experimental results also indicate that the proposed parameter identification technique is more robust to measurement noise than ILA and can provide better linearity performance when the PA nonlinearities are strong. In addition, the proposed parameter identification technique can achieve similar or better linearity performance than DLA but with a simpler identification process.