Partial Load-Pull Extrapolation Using Deep Image Completion
Partial Load-Pull Extrapolation Using Deep Image Completion
复制标题
DOI：
10.1109/wmcs49442.2020.9172302
复制
发表时间：
2020-05
期刊：
影响因子：
--
通讯作者：
Austin Egbert;A. Martone;C. Baylis;R. Marks
中科院分区：
文献类型：
--
作者：
Austin Egbert;A. Martone;C. Baylis;R. Marks
许多搜索和优化技术都受到初始起始位置选择的影响，包括功率放大器电路优化。初始起始位置的智能选择依赖于对底层搜索空间的某种理解。给定搜索空间的小样本，可以利用深度学习图像补全技术来推断对整个搜索空间的理解。该外推可以代替传统的搜索算法使用，或者可以通知用于完整优化的起始位置的选择。使用本工作的技术应用于少至9个采样测量，最佳放大器增益可以估计为小于0.6 dB的典型误差，并且相应的负载反射系数可以估计为小于0.2线性单位的典型距离，具有更大的测量样本大小的改进的精度。
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.