Collaborative Research: IMR: MM-1A: MapQ: Mapping Quality of Coverage in Mobile Broadband Networks using Latent Gaussian Process Models

大约85%的美国人拥有智能手机，并定期使用它访问互联网，查看电子邮件，使用视频会议应用程序，与医生办公室沟通，或搜索紧急问题的答案。有时这些应用程序工作得很好，有时则不然。当它们不能很好地工作时，问题往往是在使用的地方和时间的移动的宽带蜂窝网络。不幸的是，没有人完全准确地知道哪里有高质量的接入，也没有人知道哪里有接入有限或没有接入的区域。准确的覆盖质量地图将使资源能够用于最需要的领域，允许长期跟踪数字鸿沟的进展情况，并为能够适应网络质量的新应用程序提供基础。该项目旨在通过汇集多个测量数据集并为新的测量提供指导，创建准确和完整的蜂窝覆盖质量地图。这个合作项目汇集了来自格鲁吉亚理工学院和加州大学圣巴巴拉的统计建模、机器学习和移动的网络方面的专家。该项目有两个重点。第一个重点是创建数学模型，以预测蜂窝网络质量使用潜在的高斯过程中的新方法，以联合收割机测量数据集收集与不同的方法。一组模型将考虑覆盖质量如何随地理空间变化;另一组将考虑它如何随时间变化。第二个重点是以两种关键方式使用预测的覆盖质量地图，使用模型来创建覆盖质量度量，为网络用户提供有用的信息，并使用模型来指导未来的测量活动，以便不太了解的区域优先考虑。美国联邦政府和其他政府及非政府组织已拨款扩大因特网的使用。然而，由于没有人确切地知道哪里存在（或不存在）高质量的接入，因此很难将投资针对最需要的社区。如果成功，这项工作将能够告知地方，州和联邦政府应该在哪里投资，以确保所有美国人都能获得高质量的移动的互联网。因此，这些社区的居民将受益于互联网接入带来的教育、经济和医疗方面的好处。 https://sites.gatech.edu/mapping-broadband/该网站将包含项目的产品，包括数据集，模型，算法和工作产生的出版物。该网站和存储库将至少维护五年，从2022年到2027年。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

About 85% of people in the United States own a smartphone and use it to access the Internet on a regular basis, checking e-mail, using video conferencing applications, communicating with a doctor’s office, or searching for an answer to an urgent question. Sometimes these applications work well, and other times they do not. When they do not work well, the problem is often with the mobile broadband cellular network in the place and at the time of use. Unfortunately no one knows completely and accurately where high quality access exists, nor where regions of limited or no access are present. Accurate maps of coverage quality would enable resources to be directed to areas of greatest need, allow long term tracking of progress on the digital divide, and form a building block for new applications that can adapt to network quality. This project aims to create accurate and complete maps of cellular coverage quality by bringing together multiple measurement datasets and creating guidance for new measurements. This collaborative project brings together experts in statistical modeling, machine learning, and mobile networking from Georgia Institute of Technology and University of California, Santa Barbara. The project has two thrusts. The first focuses on creating mathematical models to predict cellular network quality using latent Gaussian processes in novel ways to combine measurement datasets collected with different methodologies. One set of models will consider how coverage quality varies over geographic space; the other will consider how it varies over time. The second thrust focuses on using the predicted coverage quality maps in two key ways, to use the models to create a Quality of Coverage metric that provides useful information to network users, and to use the models to guide in future measurement campaigns so that regions that are not well understood get prioritized. The United States Federal Government and other government and non-government organizations have allocated funding to broaden Internet access. However, because no one accurately knows where high quality access exists (or does not), it is difficult to target investments to communities of highest need. If successful, this work will be able to inform local, state, and federal governments about where investment should be made to ensure all Americans have access to high quality mobile Internet. As a result, residents of these communities will benefit from the educational, economic, and medical benefits that Internet access enables. https://sites.gatech.edu/mapping-broadband/ - this site will contain products of the project, including datasets, models, algorithms, and publications resulting from the work. The website and repository will be maintained for at least five years, from 2022 to 2027.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.