Collaborative Research: CNS Core: Medium: Analytics and Online Optimization at Scale for Cellular Networks

蜂窝网络已经成为社会的关键基础设施之一，用户期望可靠的连接和性能。在幕后，运营这些网络需要在从数小时到数周的时间尺度上更新数百个参数，这对于网络运营中心的工程师来说成本极高，效率极低。此外，当发生故障或性能低下时，检测和隔离根本原因再次成为一项具有挑战性但至关重要的任务。该提案的重点是有效地运营这些网络，并开发工具来检测异常，两者都使用机器学习技术。该提案的目标是开发基于在线学习、贝叶斯优化和深度学习的算法，用于参数调整和异常检测。基于与主要蜂窝提供商的合作伙伴关系以及使用真实的数据跟踪和测试平台，我们的算法和方法具有现实世界的影响。本项目利用无线理论与系统领域的知识和机器学习，开发样本有效的在线学习方法，以优化单个蜂窝基站的多维调谐参数，然后应用迁移学习进一步支持多个基站的分布式和协作参数调谐。此外，它还设计了深度压缩感知，用于蜂窝网络中的异常检测和诊断。这些推力是相互补充的：异常检测有助于在参数调整期间提供安全检查，而从参数调整中获得的见解也有助于消除歧义和诊断异常。来自美国主要蜂窝网络的真实的痕迹、仿真和试验台实验的组合被用于验证设计。该奖项反映了NSF的法定使命，并被认为值得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来支持。

Cellular networks have become one of the critical infrastructures for society, with users expecting reliable connectivity and performance. Behind the scenes, operating these networks require updating hundreds of parameters at time scales ranging from hours to weeks, which is extremely costly and inefficient for engineers at the network operations center. Further, when failures or inefficient performance occurs, detecting and isolating the root causes is again a challenging, but critical task. This proposal focuses on efficiently operating these networks and developing tools to detect anomalies, both using machine learning techniques. The goal of this proposal is to develop algorithms based on online learning, Bayesian optimization and deep learning for parameter tuning and anomaly detection. Building on partnerships with major cellular providers and the use of real data-traces and testbeds, our algorithms and approaches have real-world impact. The research outcomes are incorporated into the graduate and undergraduate curriculum.Using domain knowledge in wireless theory and systems and machine learning, this project develops sample-efficient online learning methods to optimize multi-dimensional tuning parameters in a single cellular base station, and then apply transfer learning to further support distributed and cooperative parameter tuning for multiple base stations. Moreover, it designs deep compressive sensing for anomaly detection and diagnosis in cellular networks. These thrusts are complementary to each other: anomaly detection helps to provide safety checking during parameter tuning while insights gained from parameter tuning also helps disambiguate and diagnose anomalies. A combination of real traces from a major US cellular network, simulation, and testbed experiments is used to validate the design.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.