计算机科学
深度学习
移动设备
粒度
学习迁移
人工智能
数据收集
强化学习
插值(计算机图形学)
实时计算
分布式计算
机器学习
数据挖掘
运动(物理)
统计
操作系统
数学
作者
Yini Fang,Alec F. Diallo,Chaoyun Zhang,Paul Patras
标识
DOI:10.1109/globecom46510.2021.9685804
摘要
Data-driven mobile network management hinges on accurate traffic measurements, which routinely require expensive specialized equipment and substantial local storage capabilities, and bear high data transfer overheads. To overcome these challenges, in this paper we propose Spider, a deep-learning-driven mobile traffic measurement collection and reconstruction framework, which reduces the cost of data collection while retaining state-of-the-art accuracy in inferring mobile traffic consumption with fine geographic granularity. Spider harnesses Reinforcement Learning and tackles large action spaces to train a policy network that selectively samples a minimal number of cells where data should be collected. We further introduce a fast and accurate neural model that extracts spatiotemporal correlations from historical data to reconstruct network-wide traffic consumption based on sparse measurements. Experiments we conduct with a real-world mobile traffic dataset demonstrate that Spider samples 48% fewer cells as compared to several benchmarks considered, and yields up to 67% lower reconstruction errors than state-of-the-art interpolation methods. Moreover, our framework can adapt to previously unseen traffic patterns.
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