论文标题
信号的数字前渗透性的随机优化
Stochastic optimization in digital pre-distortion of the signal
论文作者
论文摘要
在本文中，我们测试了某些现代随机优化方法和实践在应用数字前延伸问题中的性能，这是在提供无线通信的基站处理信号的重要组成部分。在研究的第一部分中，我们专注于搜索最佳性能方法及其适当的修改。在第二部分中，我们提出了新的准符合测试框架，该框架使我们能够将建模结果与现实生活中DPD原型的行为拟合，并重新测试了上一节中考虑的一些实践，并批准了该方法的优势，在现实生活中是最佳的。对于使用的模型，在标准制度中，最大程度的改进为7％，在线一个人的最大改善为5％（公制本身为对数尺度）。相应地，我们还将标准和在线制度的深度提高了3％和6％的工作时间减半。所有比较均与Adam方法进行，该方法在论文[Pasechnyuk等，2021]中被强调为DPD问题的最佳随机方法，而ADAMAX方法是在拟议的在线制度中最好的。
In this paper, we test the performance of some modern stochastic optimization methods and practices in application to digital pre-distortion problem, that is a valuable part of processing signal on base stations providing wireless communication. In first part of our study, we focus on search of the best performing method and its proper modifications. In the second part, we proposed the new, quasi-online, testing framework that allows us to fit our modelling results with the behaviour of real-life DPD prototype, retested some selected of practices considered in previous section and approved the advantages of the method occured to be the best in real-life conditions. For the used model, maximum achieved improvement in depth was 7% in standard regime and 5% in online one (metric itself is of logarithmic scale). We also achieved a halving of the working time preserving 3% and 6% improvement in depth for the standard and online regime, correspondingly. All comparisons are made to the Adam method, which was highlighted as the best stochastic method for DPD problem in paper [Pasechnyuk et al., 2021], and to the Adamax method, that is the best in the proposed online regime.
