Deep neural mobile networking

The next generation of mobile networks is set to become increasingly complex, as these struggle to accommodate tremendous data traffic demands generated by ever-more connected devices that have diverse performance requirements in terms of throughput, latency, and reliability. This makes monitoring and managing the multitude of network elements intractable with existing tools and impractical for traditional machine learning algorithms that rely on hand-crafted feature engineering. In this context, embedding machine intelligence into mobile networks becomes necessary, as this enables systematic mining of valuable information from mobile big data and automatically uncovering correlations that would otherwise have been too difficult to extract by human experts. In particular, deep learning based solutions can automatically extract features from raw data, without human expertise. The performance of artificial intelligence (AI) has achieved in other domains draws unprecedented interest from both academia and industry in employing deep learning approaches to address technical challenges in mobile networks. This thesis attacks important problems in the mobile networking area from various perspectives by harnessing recent advances in deep neural networks. As a preamble, we bridge the gap between deep learning and mobile networking by presenting a survey on the crossovers between the two areas. Secondly, we design dedicated deep learning architectures to forecast mobile traffic consumption at city scale. In particular, we tailor our deep neural network models to different mobile traffic data structures (i.e. data originating from urban grids and geospatial point-cloud antenna deployments) to deliver precise prediction. Next, we propose a mobile traffic super resolution (MTSR) technique to achieve coarse-to-fine grain transformations on mobile traffic measurements using generative adversarial network architectures. This can provide insightful knowledge to mobile operators about mobile traffic distribution, while effectively reducing the data post-processing overhead. Subsequently, the mobile traffic decomposition (MTD) technique is proposed to break the aggregated mobile traffic measurements into service-level time series, by using a deep learning based framework. With MTD, mobile operators can perform more efficient resource allocation for network slicing (i.e, the logical partitioning of physical infrastructure) and alleviate the privacy concerns that come with the extensive use of deep packet inspection. Finally, we study the robustness of network specific deep anomaly detectors with a realistic black-box threat model and propose reliable solutions for defending against attacks that seek to subvert existing network deep learning based intrusion detection systems (NIDS). Lastly, based on the results obtained, we identify important research directions that are worth pursuing in the future, including (i) serving deep learning with massive high-quality data (ii) deep learning for spatio-temporal mobile data mining (iii) deep learning for geometric mobile data mining (iv) deep unsupervised learning in mobile networks, and (v) deep reinforcement learning for mobile network control. Overall, this thesis demonstrates that deep learning can underpin powerful tools that address data-driven problems in the mobile networking domain. With such intelligence, future mobile networks can be monitored and managed more effectively and thus higher user quality of experience can be guaranteed

CloudLSTM: A Recurrent Neural Model for Spatiotemporal Point-cloud Stream Forecasting

This paper introduces CloudLSTM, a new branch of recurrent neural models tailored to forecasting over data streams generated by geospatial point-cloud sources. We design a Dynamic Point-cloud Convolution (DConv) operator as the core component of CloudLSTMs, which performs convolution directly over point-clouds and extracts local spatial features from sets of neighboring points that surround different elements of the input. This operator maintains the permutation invariance of sequence-to-sequence learning frameworks, while representing neighboring correlations at each time step -- an important aspect in spatiotemporal predictive learning. The DConv operator resolves the grid-structural data requirements of existing spatiotemporal forecasting models and can be easily plugged into traditional LSTM architectures with sequence-to-sequence learning and attention mechanisms. We apply our proposed architecture to two representative, practical use cases that involve point-cloud streams, i.e., mobile service traffic forecasting and air quality indicator forecasting. Our results, obtained with real-world datasets collected in diverse scenarios for each use case, show that CloudLSTM delivers accurate long-term predictions, outperforming a variety of competitor neural network models.Comment: 17 pages, 15 figures, AAAI'2

A Machine Learning-based Framework for Optimizing the Operation of Future Networks

5G and beyond are not only sophisticated and difficult to manage, but must also satisfy a wide range of stringent performance requirements and adapt quickly to changes in traffic and network state. Advances in machine learning and parallel computing underpin new powerful tools that have the potential to tackle these complex challenges. In this article, we develop a general machinelearning- based framework that leverages artificial intelligence to forecast future traffic demands and characterize traffic features. This makes it possible to exploit such traffic insights to improve the performance of critical network control mechanisms, such as load balancing, routing, and scheduling. In contrast to prior works that design problem-specific machine learning algorithms, our generic approach can be applied to different network functions, allowing reuse of existing control mechanisms with minimal modifications. We explain how our framework can orchestrate ML to improve two different network mechanisms. Further, we undertake validation by implementing one of these, mobile backhaul routing, using data collected by a major European operator and demonstrating a 3×reduction of the packet delay compared to traditional approaches.This work is partially supported by the Madrid Regional Government through the TAPIR-CM program (S2018/TCS-4496) and the Juan de la Cierva grant (FJCI-2017-32309). Paul Patras acknowledges the support received from the Cisco University Research Program Fund (2019-197006)

Stability Analysis and Reinforcement of the Existing Karst Cave Passing through Yujingshan Tunnel

High-speed Railway tunneling in karst terrain presents a huge challenge to the engineer including the identification, stability analysis and reinforcement of the karst cavities. The Cheng-Gui high-speed railway tunnel had to pass through the largest karst cave discovered in tunnel construction. To guaranteeing the tunnel construction safety, a series of corresponding prevention and control measures are put forward. To begin with, geological drilling, electromagnetic method and surface electrical resistivity tomography are adopted to detect and delineate the underground karst zone. Based on the detection results, this paper has put forward strategies to make the pre-support of karst cave and the main technical of those strategies include: the side-walls or in the crown was applied with shotcret (C40 steel fiber concrete); use expanding-shell pre-stressed hollow anchor rod and pre-stressed cable reinforcement; fix steel-mesh-bolting; the shotcrete sealing was applied. Moreover, if instabilities would develop in the side-walls, it should be sufficient to stabilize the cavities, to do dental cleaning of the broken rocks, and fill the voids with shotcrete or pumped lean concrete. At last, systematic grouting treatment around the excavated section, and was excavated with the layer-step method. The solutions presented here may provide guidance for the design and construction of high-speed railway tunnels to be implemented affected by karst processes. The technical validation of the proposed solutions was demonstrated by the successful completion of the Yujingshan tunnel. Please click Additional Files below to see the full abstract

Driver Behavior Recognition via Interwoven Deep Convolutional Neural Nets With Multi-Stream Inputs

Recognizing driver behaviors is becoming vital for in-vehicle systems that seek to reduce the incidence of car accidents rooted in cognitive distraction. In this paper, we harness the exceptional feature extraction abilities of deep learning and propose a dedicated Interwoven Deep Convolutional Neural Network (InterCNN) architecture to tackle the accurate classification of driver behaviors in real-time. The proposed solution exploits information from multi-stream inputs, i.e., in-vehicle cameras with different fields of view and optical flows computed based on recorded images, and merges through multiple fusion layers abstract features that it extracts. This builds a tight ensembling system, which significantly improves the robustness of the model. We further introduce a temporal voting scheme based on historical inference instances, in order to enhance accuracy. Experiments conducted with a real world dataset that we collect in a mock-up car environment demonstrate that the proposed InterCNN with MobileNet convolutional blocks can classify 9 different behaviors with 73.97% accuracy, and 5 aggregated behaviors with 81.66% accuracy. Our architecture is highly computationally efficient, as it performs inferences within 15ms, which satisfies the real-time constraints of intelligent cars. In addition, our InterCNN is robust to lossy input, as the classification remains accurate when two input streams are occluded