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

合作研究：CNS 核心：中：蜂窝网络大规模分析和在线优化

• 批准号: 2107037
• 负责人: Sanjay Shakkottai
• 金额: $ 80万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2107037&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Medium

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.

蜂窝网络已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数小时到数周的时间范围内更新数百个参数，这对于网络运营中心的工程师来说成本极高且效率低下。此外，当发生故障或低效性能时，检测和隔离根本原因又是一项具有挑战性但又至关重要的任务。该提案的重点是有效操作这些网络并开发检测异常的工具，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。我们的算法和方法建立在与主要蜂窝提供商的合作伙伴关系以及使用真实数据跟踪和测试平台的基础上，具有现实世界的影响。研究成果被纳入研究生和本科生课程。利用无线理论和系统以及机器学习领域的知识，该项目开发了样本有效的在线学习方法来优化单个蜂窝基站中的多维调谐参数，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，它还设计了用于蜂窝网络中异常检测和诊断的深度压缩感知。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。结合来自美国主要蜂窝网络、模拟和测试台实验的真实痕迹来验证设计。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Asymptotically-Optimal Gaussian Bandits with Side Observations

带有侧面观测的渐近最优高斯老虎机

• 发表时间: 2022
• 期刊: Proceedings of the 39th International Conference on Machine Learning
• 作者: Atsidakou, Alexia;Papadigenopoulos, Orestis;Caramanis, Constantine;Sanghavi, Sujay;Shakkottai, Sanjay
• 通讯作者: Shakkottai, Sanjay