Physics-Based Machine Learning Propagation Models for Wireless Access Point Placement

随着无线通信技术的不断发展，迫切需要对大量现有和新兴的无线服务进行及时且经济有效的智能规划。无线传播建模是对无线通信系统在给定环境中产生的信号电平进行预测，是这种智能规划过程的重要组成部分。这些模型可以通过基于电磁波传播物理的数值算法，如射线追踪，来推导出来。然而，基于物理的方法需要高水平的相关专业知识和大量的计算资源。虽然在几年前，以准确性换取效率可能是一个可接受的解决方案，但新兴的、日益复杂的无线服务领域要求在传播建模方面取得相应的进步：高保真度、简单推导、计算效率高的模型。该项目的主要目标是利用科学机器学习的进步来加速复杂的、基于物理的传播分析方法在无线系统设计中的应用，促进无线工程团队广泛采用这些方法。因此，我们的研究可以克服传统传播模型的准确性和效率之间的二分法，从而为当前，新兴和未来几代无线通信系统提供更具成本效益，快速和稳健的设计。该项目是基于申请人在多伦多大学的研究小组之间的合作伙伴关系，该小组在无线通信系统的传播建模方面有着长期的记录，目前专注于该领域的机器学习应用，iBwave是一家加拿大公司，致力于改进大型场所的无线网络设计，如体育场，商场和多层办公空间。

The continuous expansion of wireless communication technologies creates a pressing need for intelligent planning of a plethora of existing and emerging wireless services, in a timely and cost-effective manner. Wireless propagation modeling, which is the prediction of the signal levels generated by a wireless communication system in a given environment, is an essential element of such an intelligent planning process. These models can be deduced by numerical algorithms such as ray-tracing, which are based on the physics of electromagnetic wave propagation. However, physics-based methods require a high level of relevant expertise and significant computational resources. While trading accuracy for efficiency could have been an acceptable solution a few years ago, the emerging, increasingly complex landscape of wireless services requires a proportional advancement in the state of the art in propagation modeling: high-fidelity, simple to derive, computationally efficient models. The main goal of this project is to leverage advances in scientific machine learning to accelerate the application of sophisticated, physics-based propagation analysis methods to wireless system design, facilitating their broad adoption by wireless engineering teams. Therefore, our research can overcome the traditional dichotomy between accuracy and efficiency of propagation models, leading to more cost-effective, fast and robust design for current, emerging and future generations of wireless communication systems. The project is based on a partnership between the applicant's research group at the University of Toronto, a group with a long track record in propagation modeling for wireless communication systems currently focusing on machine learning applications to this area, with iBwave, a Canadian company that is dedicated to improving wireless network design in large venues such as stadiums, malls, and multi-floor office spaces.