Forecasting with high accuracy the volume of data traffic that mobile users
will consume is becoming increasingly important for precision traffic
engineering, demand-aware network resource allocation, as well as public
transportation. Measurements collection in dense urban deployments is however
complex and expensive, and the post-processing required to make predictions is
highly non-trivial, given the intricate spatio-temporal variability of mobile
traffic due to user mobility. To overcome these challenges, in this paper we
harness the exceptional feature extraction abilities of deep learning and
propose a Spatio-Temporal neural Network (STN) architecture purposely designed
for precise network-wide mobile traffic forecasting. We present a mechanism
that fine tunes the STN and enables its operation with only limited ground
truth observations. We then introduce a Double STN technique (D-STN), which
uniquely combines the STN predictions with historical statistics, thereby
making faithful long-term mobile traffic projections. Experiments we conduct
with real-world mobile traffic data sets, collected over 60 days in both urban
and rural areas, demonstrate that the proposed (D-)STN schemes perform up to
10-hour long predictions with remarkable accuracy, irrespective of the time of
day when they are triggered. Specifically, our solutions achieve up to 61%
smaller prediction errors as compared to widely used forecasting approaches,
while operating with up to 600 times shorter measurement intervals.

本文提出一种专门用于精确预测网络范围内移动数据流量的时空神经网络（STN）结构和双 STN 技术（D-STN），通过在城市和农村地区 60 天的真实移动流量数据集上进行的实验，证明了所提出的（D-）STN 方法可以实现高达 10 小时的精确预测，并且与其他流行的预测方法相比，可以获得高达 61% 的更小预测误差。

使用深度时空神经网络进行长期移动流量预测

Long-Term Mobile Traffic Forecasting Using Deep Spatio-Temporal Neural  Networks

Large-scale mobile traffic analytics is becoming essential to digital
infrastructure provisioning, public transportation, events planning, and other
domains. Monitoring city-wide mobile traffic is however a complex and costly
process that relies on dedicated probes. Some of these probes have limited
precision or coverage, others gather tens of gigabytes of logs daily, which
independently offer limited insights. Extracting fine-grained patterns involves
expensive spatial aggregation of measurements, storage, and post-processing. In
this paper, we propose a mobile traffic super-resolution technique that
overcomes these problems by inferring narrowly localised traffic consumption
from coarse measurements. We draw inspiration from image processing and design
a deep-learning architecture tailored to mobile networking, which combines
Zipper Network (ZipNet) and Generative Adversarial neural Network (GAN) models.
This enables to uniquely capture spatio-temporal relations between traffic
volume snapshots routinely monitored over broad coverage areas
(`low-resolution') and the corresponding consumption at 0.05 km $^2$ level
(`high-resolution') usually obtained after intensive computation. Experiments
we conduct with a real-world data set demonstrate that the proposed
ZipNet(-GAN) infers traffic consumption with remarkable accuracy and up to
100$\times$ higher granularity as compared to standard probing, while
outperforming existing data interpolation techniques. To our knowledge, this is
the first time super-resolution concepts are applied to large-scale mobile
traffic analysis and our solution is the first to infer fine-grained urban
traffic patterns from coarse aggregates.

本文提出了一种针对城市移动流量分析的移动流量超分辨率技术，利用深度学习架构为移动网络设计了定制 ZipNet 和 GAN 模型，通过推断出从粗浅测量中获取的狭窄本地流量消耗， 能够唯一捕捉广泛覆盖区域上例行监测的流量量快照和相应消费的高分辨率的 spatio-temporal 关系，并通过真实数据集的实验表明，该方案是应用超分辨率概念大规模移动流量分析的首次尝试，与标准探测相比，精度高并且比现有的数据插值技术更好。

ZipNet-GAN：基于生成对抗神经网络推断细粒度移动流量模式

ZipNet-GAN: Inferring Fine-grained Mobile Traffic Patterns via a  Generative Adversarial Neural Network

A large fraction of Internet traffic is now driven by requests from mobile
devices with relatively small screens and often stringent bandwidth
requirements. Due to these factors, it has become the norm for modern
graphics-heavy websites to transmit low-resolution, low-bytecount image
previews (thumbnails) as part of the initial page load process to improve
apparent page responsiveness. Increasing thumbnail compression beyond the
capabilities of existing codecs is therefore a current research focus, as any
byte savings will significantly enhance the experience of mobile device users.
Toward this end, we propose a general framework for variable-rate image
compression and a novel architecture based on convolutional and deconvolutional
LSTM recurrent networks. Our models address the main issues that have prevented
autoencoder neural networks from competing with existing image compression
algorithms: (1) our networks only need to be trained once (not per-image),
regardless of input image dimensions and the desired compression rate; (2) our
networks are progressive, meaning that the more bits are sent, the more
accurate the image reconstruction; and (3) the proposed architecture is at
least as efficient as a standard purpose-trained autoencoder for a given number
of bits. On a large-scale benchmark of 32$\times$32 thumbnails, our LSTM-based
approaches provide better visual quality than (headerless) JPEG, JPEG2000 and
WebP, with a storage size that is reduced by 10% or more.

本文提出了一种基于卷积 LSTM 神经网络的可变比率图像压缩框架和架构，可以用于压缩缩略图以改善移动设备用户的体验，经测试在 32×32 的缩略图上，与 JPEG、JPEG2000 和 WebP 相比，我们的方法在相同存储大小下提供更好的视觉质量。