The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper, we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

/pdf/deep-learning-in-mobile-and-wireless-networking-a-survey-ct8cy2kuqi.pdf

Deep Learning in Mobile and Wireless Networking: A Survey

Mobile crowdsensing (MCS) has gained significant attention in recent years and has become an appealing paradigm for urban sensing. For data collection, MCS systems rely on contribution from mobile devices of a large number of participants or a crowd. Smartphones, tablets, and wearable devices are deployed widely and already equipped with a rich set of sensors, making them an excellent source of information. Mobility and intelligence of humans guarantee higher coverage and better context awareness if compared to traditional sensor networks. At the same time, individuals may be reluctant to share data for privacy concerns. For this reason, MCS frameworks are specifically designed to include incentive mechanisms and address privacy concerns. Despite the growing interest in the research community, MCS solutions need a deeper investigation and categorization on many aspects that span from sensing and communication to system management and data storage. In this paper, we take the research on MCS a step further by presenting a survey on existing works in the domain and propose a detailed taxonomy to shed light on the current landscape and classify applications, methodologies, and architectures. Our objective is not only to analyze and consolidate past research but also to outline potential future research directions and synergies with other research areas.

/pdf/a-survey-on-mobile-crowdsensing-systems-challenges-solutions-224oot2vez.pdf

A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

As one of the key communication scenarios in the fifth-generation and also the sixth-generation (6G) mobile communication networks, ultrareliable and low-latency communications (URLLCs) will be central for the development of various emerging mission-critical applications. State-of-the-art mobile communication systems do not fulfill the end-to-end delay and overall reliability requirements of URLLCs. In particular, a holistic framework that takes into account latency, reliability, availability, scalability, and decision-making under uncertainty is lacking. Driven by recent breakthroughs in deep neural networks, deep learning algorithms have been considered as promising ways of developing enabling technologies for URLLCs in future 6G networks. This tutorial illustrates how domain knowledge (models, analytical tools, and optimization frameworks) of communications and networking can be integrated into different kinds of deep learning algorithms for URLLCs. We first provide some background of URLLCs and review promising network architectures and deep learning frameworks for 6G. To better illustrate how to improve learning algorithms with domain knowledge, we revisit model-based analytical tools and cross-layer optimization frameworks for URLLCs. Following this, we examine the potential of applying supervised/unsupervised deep learning and deep reinforcement learning in URLLCs and summarize related open problems. Finally, we provide simulation and experimental results to validate the effectiveness of different learning algorithms and discuss future directions.

A Tutorial on Ultrareliable and Low-Latency Communications in 6G: Integrating Domain Knowledge Into Deep Learning

Touch is currently seen as the modality that will complement audition and vision as a third media stream over the Internet in a variety of future haptic applications which will allow full immersion and that will, in many ways, impact society. Nevertheless, the high requirements of these applications demand networks which allow ultra-reliable and low-latency communication for the challenging task of applying the required quality of service for maintaining the user’s quality of experience at optimum levels. In this survey, we enlist, discuss, and evaluate methodologies and technologies of the necessary infrastructure for haptic communication. Furthermore, we focus on how the fifth generation of mobile networks will allow haptic applications to take life, in combination with the haptic data communication protocols, bilateral teleoperation control schemes and haptic data processing needed. Finally, we state the lessons learned throughout the surveyed research material along with the future challenges and infer our conclusions.

/pdf/toward-haptic-communications-over-the-5g-tactile-internet-1ye12g467d.pdf

Toward Haptic Communications Over the 5G Tactile Internet

Over the last decade, a significant amount of effort has been invested on architecting agile and adaptive management solutions in support of autonomic, self-managing networks. Autonomic networking calls for automated decisions for management actions. This can be realized through a set of pre-defined network management policies engineered from human expert knowledge. However, engineering sufficiently accurate knowledge considering the high complexity of today's networking environment is a difficult task. This has been a particularly limiting factor in the practical deployment of autonomic systems. ML is a powerful technique for extracting knowledge from data. However, there has been little evidence of its application in realizing practical management solutions for autonomic networks. Recent advances in network softwarization and programmability through SDN and NFV, the proliferation of new sources of data, and the availability of lowcost and seemingly infinite storage and compute resource from the cloud are paving the way for the adoption of ML to realize cognitive network management in support of autonomic networking. This article is intended to stimulate thought and foster discussion on how to defeat the bottlenecks that are limiting the wide deployment of autonomic systems, and the role that ML can play in this regard.

Machine Learning for Cognitive Network Management

A growing trend for information technology is to not just react to changes, but anticipate them as much as possible. This paradigm made modern solutions, such as recommendation systems, a ubiquitous presence in today’s digital transactions. Anticipatory networking extends the idea to communication technologies by studying patterns and periodicity in human behavior and network dynamics to optimize network performance. This survey collects and analyzes recent papers leveraging context information to forecast the evolution of network conditions and, in turn, to improve network performance. In particular, we identify the main prediction and optimization tools adopted in this body of work and link them with objectives and constraints of the typical applications and scenarios. Finally, we consider open challenges and research directions to make anticipatory networking part of next generation networks.

/pdf/a-survey-of-anticipatory-mobile-networking-context-based-2yy8uql5nk.pdf

A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

Anticipatory networking is a recent branch of network optimization based on prediction of the system state. Our work specifically tackles prediction-driven resource allocation for mobile networks. While some anticipatory networking concepts have been proposed in the literature, understanding of the potential real world gains is so far very limited. Future mobile networks will likely integrate such mechanisms, and thus it is of paramount importance to understand the actual performance improvements and in which scenarios they can be realized. Analyzing a month of LTE control channel information collected in four locations, we show how anticipatory networking can enhance current LTE networks. First, we propose a comprehensive optimization framework encompassing different forecasting solutions. Then, we provide a thorough analysis of the aggregated network traffic and the contributions of individual users. In particular, we show that predictable traffic accounts for more than 95 percent of the total traffic volume and that simple prediction and optimization techniques allow network operators to save 50 percent of the resources and/or on average more than double the offered data rate in our data set.

Data-Driven Evaluation of Anticipatory Networking in LTE Networks

A growing trend for information technology is to not just react to changes, but anticipate them as much as possible. This paradigm made modern solutions, such as recommendation systems, a ubiquitous presence in today's digital transactions. Anticipatory networking extends the idea to communication technologies by studying patterns and periodicity in human behavior and network dynamics to optimize network performance. This survey collects and analyzes recent papers leveraging context information to forecast the evolution of network conditions and, in turn, to improve network performance. In particular, we identify the main prediction and optimization tools adopted in this body of work and link them with objectives and constraints of the typical applications and scenarios. Finally, we consider open challenges and research directions to make anticipatory networking part of next generation networks.

One of the most critical aspects of anticipatory networking is assuming that future system conditions can be estimated. In this paper we address how accurate the current state of the art predictors are in providing a forecast of short term throughput. We propose a simple model for the short term prediction error based on Gaussian Random Walks that allows for mathematical analysis of the impact of imperfect future
knowledge on network optimization.

http://eprints.networks.imdea.org/id/document/2876

Modelling Throughput Prediction Errors as Gaussian Random Walks

Anticipatory networking is a recent branch of network optimization that exploits contextual information to improve resource allocation decisions based on prediction. While some anticipatory networking concepts have been proposed in the literature, understanding of the potential real-world gains is so far very limited. Future mobile networks will likely integrate such mechanisms, and thus it is of paramount importance to understand the actual performance improvements and in which scenarios they can be realized. Analyzing a month-worth of LTE control channel information collected in four urban locations, we show how anticipatory networking can enhance current LTE networks. First, we propose a comprehensive optimization framework encompassing different forecasting solutions. Then, we provide a thorough analysis of the aggregated network traffic and the contributions of individual users. In particular, we show that predictable traffic accounts for more than 95% of the total traffic volume and that simple prediction and optimization techniques allow network operators to save 50% of the resources and/or on average more than double the offered data rate in our data set.

Joerg Widmer

Papers

A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

Data-Driven Evaluation of Anticipatory Networking in LTE Networks

A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

Modelling Throughput Prediction Errors as Gaussian Random Walks

Data-Driven Evaluation of Anticipatory Networking in LTE Networks