Using data from mobile network utilization in Norway, we showcase the possibility of monitoring the state of communication and mobility networks with a non-invasive, low-cost method. This method transforms the network data into a model within the framework of reservoir computing and then measures the model's performance on proxy tasks. Experimentally, we show how the performance on these proxies relates to the state of the network. A key advantage of this approach is that it uses readily available data sets and leverages the reservoir computing framework for an inexpensive and largely agnostic method. Data from mobile network utilization is available in an anonymous, aggregated form with multiple snapshots per day. This data can be treated like a weighted network. Reservoir computing allows the use of weighted, but untrained networks as a machine learning tool. The network, initialized as a so-called echo state network (ESN), projects incoming signals into a higher dimensional space, on which a single trained layer operates. This consumes less energy than deep neural networks in which every weight of the network is trained. We use neuroscience inspired tasks and trained our ESN model to solve them. We then show how the performance depends on certain network configurations and also how it visibly decreases when perturbing the network. While this work serves as proof of concept, we believe it can be elevated to be used for near-real-time monitoring as well as the identification of possible weak spots of both mobile communication networks as well as transportation networks.

翻译：利用挪威移动网络使用数据，我们展示了一种非侵入式、低成本方法监测通信与移动网络状态的可能性。该方法将网络数据转化为储层计算框架内的模型，随后测量模型在代理任务上的性能表现。实验表明，这些代理任务的性能与网络状态存在关联。该方法的关键优势在于使用易于获取的数据集，并借助储层计算框架实现低成本且高度无关性的监测。移动网络使用数据以匿名聚合形式提供，每日包含多个快照，可视为加权网络进行处理。储层计算允许将加权但未经训练的网络作为机器学习工具使用。网络初始化为所谓的回声状态网络（ESN），将输入信号映射到高维空间，由单个训练层进行处理。相较于需要训练网络所有权重的深度神经网络，这种方法能耗更低。我们采用神经科学启发的任务训练ESN模型，并展示性能如何依赖于特定网络配置，以及在网络受扰动时性能如何显著下降。尽管本研究作为概念验证，但我们相信该方法可进一步发展为近实时监测工具，用于识别移动通信网络及交通网络中可能存在的薄弱环节。