IMR: MM-1C: Learning-driven Models for 5G Internet Measurements

下一代(NextG)蜂窝网络将从根本上改变当今人们对互联网的看法，它具有丰富的令人兴奋的应用程序，可以提供“智能服务”。利用人工智能(AI)和机器学习(ML)的重大进步，设想的下一代互联网测量基础设施将显著增强网络监控、管理和部署。为了实现这一目标，出现了艰巨的挑战，因为测量可能是多样化的、复杂的，甚至是不可用的。在此背景下，本项目倡导一种学习驱动的方法，用于系统、健壮和大规模的NextG互联网测量基础设施。这一方法将从进行的广泛、深入的测量中获得信息，并将进一步利用所开发的平台和测量工具。在三个相互交织的研究推进中追求关键的智力进步：T1)新颖的高斯过程集合，以建模丰富的、跨层的特征，以及环境、用户和应用动态；T2)创新的贝叶斯主动采样，以指导测量过程；以及，T3)开创性的工具，以去偏、去噪，并整合可能包含缺失特征(例如，由于隐私保护)的众包测量；并且可能是有偏见的、相互冲突的，甚至是对抗性的。结果将与可用的测量平台集成，以改进采样过程、数据管理和分析，并将不断改进和验证。这项研究将通过许多尚未想象的新应用和服务，广泛影响下一代网络的测量、设计、部署和运营，从而为整个社会带来重大好处。该项目还提供机会，让本科生和K-12年级的学生--特别是妇女和代表性不足的少数群体--参与综合研究、教育和外联活动。通过与Industry的合作，转移的技术将影响NextG网络的发展。此外，新的互联网测量工具将配备开源软件，以公开提供模型、代码、数据集、评估测试和相关文物。该项目的URL https://spincom.umn.edu/research/currently-funded-projects/nsf2220292This奖反映了美国国家科学基金会的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Enriched with exciting applications providing `smart services,’ next-generation (NextG) cellular networks will fundamentally transform today’s perception of the Internet. Leveraging major advances in artificial intelligence (AI) and machine learning (ML), the envisioned NextG Internet measurement infrastructure will markedly enhance network monitoring, management and deployment. Toward this goal, formidable challenges emerge as measurements can be diverse, complex, and even unavailable. In this context, the present project advocates a learning-driven methodology for systematic, robust, and large-scale NextG Internet measurement infrastructure. This methodology will be informed by extensive, in-depth measurements conducted, and will further utilize the platform and measurement tools developed.Key intellectual advances are pursued in three intertwined research thrusts: T1) novel ensembles of Gaussian Processes to model rich, cross-layer features, and environment, user, and application dynamics; T2) innovative Bayesian active sampling to guide the measurement process; and, T3) path-breaking tools to de-bias, de-noise, and integrate crowd-sourced measurements that may contain missing features (e.g., due to privacy protection); and may be biased, conflicting, or even adversarial. Outcomes will be integrated with an available measurement platform to improve the sampling process, data curation, and analytics that will be continually refined and validated.This research will broadly impact the measurement, design, deployment, and operation of NextG networks, through novel applications and services, many yet to be imagined, thereby bringing significant benefits to the society at large. The project also offers opportunities for engaging undergraduate and K-12 students -- especially women and underrepresented minority groups -- in integrated research, education, and outreach activities. Through collaborations with Industry, the technology transferred will influence the development of NextG networks. In addition, the novel Internet measurement tools will come with open-source software to render the models, codes, datasets, evaluation tests and pertinent artifacts, publicly available. Project’s URL https://spincom.umn.edu/research/currently-funded-projects/nsf2220292This 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.