Partial Load-Pull Extrapolation Using Deep Image Completion

Many search and optimization techniques are influenced by the choice of initial starting location, including power amplifier circuit optimization. Intelligent choice of an initial starting location relies upon some understanding of the underlying search space. Given a small sample of the search space, deep learning image completion techniques can be utilized to extrapolate an understanding of the entire search space. This extrapolation can be used in lieu of a traditional search algorithm or can inform the selection of a starting location for a complete optimization. Using the techniques of this work applied to as few as nine sampled measurements, the optimum amplifier gain can be estimated with a typical error of <; 0.6 dB and the corresponding load reflection coefficient can be estimated to a typical distance of <; 0.2 linear units, with improved accuracy with larger measurement sample sizes.