877 research outputs found
Living on the Edge: The Role of Proactive Caching in 5G Wireless Networks
This article explores one of the key enablers of beyond G wireless
networks leveraging small cell network deployments, namely proactive caching.
Endowed with predictive capabilities and harnessing recent developments in
storage, context-awareness and social networks, peak traffic demands can be
substantially reduced by proactively serving predictable user demands, via
caching at base stations and users' devices. In order to show the effectiveness
of proactive caching, we examine two case studies which exploit the spatial and
social structure of the network, where proactive caching plays a crucial role.
Firstly, in order to alleviate backhaul congestion, we propose a mechanism
whereby files are proactively cached during off-peak demands based on file
popularity and correlations among users and files patterns. Secondly,
leveraging social networks and device-to-device (D2D) communications, we
propose a procedure that exploits the social structure of the network by
predicting the set of influential users to (proactively) cache strategic
contents and disseminate them to their social ties via D2D communications.
Exploiting this proactive caching paradigm, numerical results show that
important gains can be obtained for each case study, with backhaul savings and
a higher ratio of satisfied users of up to and , respectively.
Higher gains can be further obtained by increasing the storage capability at
the network edge.Comment: accepted for publication in IEEE Communications Magazin
Will 5G See its Blind Side? Evolving 5G for Universal Internet Access
Internet has shown itself to be a catalyst for economic growth and social
equity but its potency is thwarted by the fact that the Internet is off limits
for the vast majority of human beings. Mobile phones---the fastest growing
technology in the world that now reaches around 80\% of humanity---can enable
universal Internet access if it can resolve coverage problems that have
historically plagued previous cellular architectures (2G, 3G, and 4G). These
conventional architectures have not been able to sustain universal service
provisioning since these architectures depend on having enough users per cell
for their economic viability and thus are not well suited to rural areas (which
are by definition sparsely populated). The new generation of mobile cellular
technology (5G), currently in a formative phase and expected to be finalized
around 2020, is aimed at orders of magnitude performance enhancement. 5G offers
a clean slate to network designers and can be molded into an architecture also
amenable to universal Internet provisioning. Keeping in mind the great social
benefits of democratizing Internet and connectivity, we believe that the time
is ripe for emphasizing universal Internet provisioning as an important goal on
the 5G research agenda. In this paper, we investigate the opportunities and
challenges in utilizing 5G for global access to the Internet for all (GAIA). We
have also identified the major technical issues involved in a 5G-based GAIA
solution and have set up a future research agenda by defining open research
problems
A survey of machine learning techniques applied to self organizing cellular networks
In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future
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