Ultra-wide Band mm-Wave Components Design Based on Machine Learning Techniques

未来的通信系统正朝着利用毫米波频段为短距离通信提供大带宽的方向发展。此类网络是支持 5G 标准和物联网应用的骨干网的重要组成部分。此类应用将导致接入点数量显着增加。当前的设计方法无法以此类应用所需的速度提供大量设计。 Scientific Microware Corporation (SMC) 是全球知名的微波和毫米波器件设计和制造领域的加拿大公司，其毫米波器件的设计过程非常耗时，无法满足预期的高需求。因此，本研究的目的是解决这个问题，并提出一些基于机器学习领域最新进展的解决方案。**在本研究中，我们的目标是研究机器学习技术和毫米波元件设计**流程，以提出加快设计阶段的解决方案，特别是通过保存 SMC** 之前完成的设计的历史数据来自动化设计流程的解决方案，以便将来利用它们。这将使设计过程自动化，并缩短**接收需求和生产定制产品之间的时间。

Future communication systems are heading toward the utilization of the mm-wave bands to provide a large**bandwidth for short distance communication. This type of networks is an essential part of the backbone that**supports both 5G standard and IOT applications. Such applications will result in a significant increase in the**number of access points. The current design methodologies cannot provide a large number of designs as fast as**required for such applications. At Scientific Microware Corporation (SMC), which is a worldwide known**Canadian corporation in the field of design and manufacturing of microwave and mm-wave components, the**design procedure of mm-wave components is time consuming in a way that it is not suitable for expected high**demand. Thus, the objective of this research is to address this problem and to propose some solutions that are**based on the latest advancement in the field of machine learning.**In this research, we are targeting to study machine learning techniques, and the mm-wave component design**process, in order to propose solutions that will speed up the design phase, and specifically, solutions that will**automate the design process by saving the historical data of previous designs that were accomplished at SMC**in order to exploit them in the future. This will automate the design process, and will shorten the time between**the reception of a demand and the production of a custom-made product.