To date, power electronics parameter design tasks are usually tackled using
detailed optimization approaches with detailed simulations or using brute force
grid search grid search with very fast simulations. A new method, named
"Continuously Adapting Random Sampling" (CARS) is proposed, which provides a
continuous method in between. This allows for very fast, and / or large amounts
of simulations, but increasingly focuses on the most promising parameter
ranges. Inspirations are drawn from multi-armed bandit research and lead to
prioritized sampling of sub-domains in one high-dimensional parameter tensor.
Performance has been evaluated on three exemplary power electronic use-cases,
where resulting designs appear competitive to genetic algorithms, but
additionally allow for highly parallelizable simulation, as well as continuous
progression between explorative and exploitative settings.

一种名为 “持续自适应随机采样”（CARS）的新方法被提出，用于处理电力电子参数设计任务，它提供了一种介于详细优化方法和快速模拟方法之间的连续方法，能够快速地进行大量模拟，并对最有前途的参数范围进行重点采样。这种方法受到多臂赌博机研究的启发，通过优先采样子域来高效处理高维参数张量。在三个电力电子应用案例上进行了性能评估，得到的设计结果竞争力强于遗传算法，并且还能够支持高度并行化的模拟，以及探索性和开发性设置之间的连续进展。

连续自适应随机抽样（CARS）用于电力电子参数设计

Continuously Adapting Random Sampling (CARS) for Power Electronics  Parameter Design

The stringent performance requirements of future wireless networks, such as
ultra-high data rates, extremely high reliability and low latency, are spurring
worldwide studies on defining the next-generation multiple-input
multiple-output (MIMO) transceivers. For the design of advanced transceivers in
wireless communications, optimization approaches often leading to iterative
algorithms have achieved great success for MIMO transceivers. However, these
algorithms generally require a large number of iterations to converge, which
entails considerable computational complexity and often requires fine-tuning of
various parameters. With the development of deep learning, approximating the
iterative algorithms with deep neural networks (DNNs) can significantly reduce
the computational time. However, DNNs typically lead to black-box solvers,
which requires amounts of data and extensive training time. To further overcome
these challenges, deep-unfolding has emerged which incorporates the benefits of
both deep learning and iterative algorithms, by unfolding the iterative
algorithm into a layer-wise structure analogous to DNNs. In this article, we
first go through the framework of deep-unfolding for transceiver design with
matrix parameters and its recent advancements. Then, some endeavors in applying
deep-unfolding approaches in next-generation advanced transceiver design are
presented. Moreover, some open issues for future research are highlighted.

本文介绍了深度展开（deep-unfolding）在矩阵参数下的收发机设计以及其最新进展，阐述了将其应用于下一代先进收发机设计方面的努力，并提出了未来研究的一些开放性问题。