These days, mobile devices usually have multiple network interfaces and there are many usable access networks around the devices. To utilize a wide range of network options properly and make decisions more intelligently, the mobile devices should be able to understand networks' situations autonomously. The current mobile devices have powerful computing power and are able to collect various network information, and people nowadays almost always carry their mobile devices. Thus, the mobile devices can be utilized to figure out practical quality of service/experience and infer the network situation/context. However, networks have become not only larger but also more complex and dynamic than in the past, so it is hard to devise models, algorithms, or system platforms for mobile devices to understand such complex and diverse networks. To overcome this limitation, we leverage deep learning to devise a mobile device-centric approach to identifying problem spot having the most likely cause of network quality degradation, MoNPI. By using MoNPI, mobile devices are able to identify the network problem spot, which is like a black box to end nodes heretofore. Mobile devices with MoNPI are able to understand networks' situations and thus take a more proper action.

中文翻译：

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使用深度学习识别网络中问题点的以移动设备为中心的方法

如今，移动设备通常具有多个网络接口，并且设备周围有许多可用的接入网络。为了正确利用广泛的网络选项并更智能地做出决策，移动设备应该能够自主了解网络情况。当前的移动设备具有强大的计算能力，能够收集各种网络信息，现在人们几乎总是随身携带移动设备。因此，移动设备可用于计算实际的服务质量/体验质量并推断网络情况/上下文。然而，网络不仅变得更大，而且比过去更加复杂和动态，因此很难为移动设备设计模型、算法或系统平台来理解这种复杂多样的网络。为了克服这一限制，我们利用深度学习设计了一种以移动设备为中心的方法来识别最有可能导致网络质量下降的问题点 MoNPI。通过使用 MoNPI，移动设备能够识别网络问题点，这是迄今为止终端节点的黑匣子。具有 MoNPI 的移动设备能够了解网络的情况，从而采取更适当的行动。