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

点击翻译按钮获取中文摘要

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.