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Scalable Models for Microwave Antennas Using Neural Networks and Space Mapping**

使用神经网络和空间映射的微波天线的可扩展模型**

基本信息

批准号：
533695-2018
负责人：
Zhang, Qijun
金额：
$ 1.82万
依托单位：
Carleton University
依托单位国家：
加拿大
项目类别：
Engage Grants Program
财政年份：
2018
资助国家：
加拿大
起止时间：
2018-01-01 至 2019-12-31
项目状态：
已结题

来源：
https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=669698
关键词：
Scalable Models Microwave Antennas Using

项目摘要

Antenna design is important for realizing wireless networks in many applications such as 5G, IoT and autonomous vehicles networks. With drastic advances in electromagnetic simulations, electromagnetic based antenna design has become widely adopted in industry. However, electromagnetic based optimization remains computationally expensive. The problem faced by our industry partner is even more challenging. It is constantly required to design different antennas for different frequency bands. This means that the expensive electromagnetic optimization has to be performed again and again whenever antenna specifications are changed. In this project, we develop scalable antenna models such that previous antenna design results can be re-used to help produce new antenna design solutions for different frequency bands. Technical challenges to be addressed includes the establishment of unknown nonlinear relationships between design parameters and antenna performance factors, the expense of generating many data samples from electromagnetic data generation and more. We will develop new approaches to combine space mapping and neural network techniques to solve this modeling problem. The project will be carried out with strong collaboration with our industry partner. The success of this work can help significantly increase the efficiency of antenna design, and strengthen the Canadian competitiveness in this globally competitive field.******

天线设计对于在5G、物联网和自动驾驶汽车网络等许多应用中实现无线网络至关重要。随着电磁仿真技术的飞速发展，基于电磁场的天线设计在工业上得到了广泛的应用。然而，基于电磁的优化仍然是计算昂贵的。我们的行业合作伙伴面临的问题更具挑战性。不断地需要为不同的频带设计不同的天线。这意味着每当天线规格改变时，必须一次又一次地执行昂贵的电磁优化。在这个项目中，我们开发可扩展的天线模型，使以前的天线设计结果可以重复使用，以帮助产生新的天线设计解决方案，为不同的频段。需要解决的技术挑战包括在设计参数和天线性能因素之间建立未知的非线性关系，从电磁数据生成中生成许多数据样本的费用等等。我们将开发新的方法来结合联合收割机空间映射和神经网络技术来解决这个建模问题。该项目将与我们的行业合作伙伴密切合作进行。这项工作的成功可以帮助显著提高天线设计的效率，并加强加拿大在这一全球竞争领域的竞争力。