1,523 research outputs found
Big Data Caching for Networking: Moving from Cloud to Edge
In order to cope with the relentless data tsunami in wireless networks,
current approaches such as acquiring new spectrum, deploying more base stations
(BSs) and increasing nodes in mobile packet core networks are becoming
ineffective in terms of scalability, cost and flexibility. In this regard,
context-aware G networks with edge/cloud computing and exploitation of
\emph{big data} analytics can yield significant gains to mobile operators. In
this article, proactive content caching in G wireless networks is
investigated in which a big data-enabled architecture is proposed. In this
practical architecture, vast amount of data is harnessed for content popularity
estimation and strategic contents are cached at the BSs to achieve higher
users' satisfaction and backhaul offloading. To validate the proposed solution,
we consider a real-world case study where several hours of mobile data traffic
is collected from a major telecom operator in Turkey and a big data-enabled
analysis is carried out leveraging tools from machine learning. Based on the
available information and storage capacity, numerical studies show that several
gains are achieved both in terms of users' satisfaction and backhaul
offloading. For example, in the case of BSs with of content ratings
and Gbyte of storage size ( of total library size), proactive
caching yields of users' satisfaction and offloads of the
backhaul.Comment: accepted for publication in IEEE Communications Magazine, Special
Issue on Communications, Caching, and Computing for Content-Centric Mobile
Network
On the traffic offloading in Wi-Fi supported heterogeneous wireless networks
Heterogeneous small cell networks (HetSNet) comprise several low power, low cost (SBSa), (D2D) enabled links wireless-fidelity (Wi-Fi) access points (APs) to support the existing macrocell infrastructure, decrease over the air signaling and energy consumption, and increase network capacity, data rate and coverage. This paper presents an active user dependent path loss (PL) based traffic offloading (TO) strategy for HetSNets and a comparative study on two techniques to offload the traffic from macrocell to (SBSs) for indoor environments: PL and signal-to-interference ratio (SIR) based strategies. To quantify the improvements, the PL based strategy against the SIR based strategy is compared while considering various macrocell and (SBS) coverage areas and traffic–types. On the other hand, offloading in a dense urban setting may result in overcrowding the (SBSs). Therefore, hybrid traffic–type driven offloading technologies such as (WiFi) and (D2D) were proposed to en route the delay tolerant applications through (WiFi) (APs) and (D2D) links. It is necessary to illustrate the impact of daily user traffic profile, (SBSs) access schemes and traffic–type while deciding how much of the traffic should be offloaded to (SBSs). In this context, (AUPF) is introduced to account for the population of active small cells which depends on the variable traffic load due to the active users
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
Big Data Meets Telcos: A Proactive Caching Perspective
Mobile cellular networks are becoming increasingly complex to manage while
classical deployment/optimization techniques and current solutions (i.e., cell
densification, acquiring more spectrum, etc.) are cost-ineffective and thus
seen as stopgaps. This calls for development of novel approaches that leverage
recent advances in storage/memory, context-awareness, edge/cloud computing, and
falls into framework of big data. However, the big data by itself is yet
another complex phenomena to handle and comes with its notorious 4V: velocity,
voracity, volume and variety. In this work, we address these issues in
optimization of 5G wireless networks via the notion of proactive caching at the
base stations. In particular, we investigate the gains of proactive caching in
terms of backhaul offloadings and request satisfactions, while tackling the
large-amount of available data for content popularity estimation. In order to
estimate the content popularity, we first collect users' mobile traffic data
from a Turkish telecom operator from several base stations in hours of time
interval. Then, an analysis is carried out locally on a big data platform and
the gains of proactive caching at the base stations are investigated via
numerical simulations. It turns out that several gains are possible depending
on the level of available information and storage size. For instance, with 10%
of content ratings and 15.4 Gbyte of storage size (87% of total catalog size),
proactive caching achieves 100% of request satisfaction and offloads 98% of the
backhaul when considering 16 base stations.Comment: 8 pages, 5 figure
Performance Analysis of Micro Unmanned Airborne Communication Relays for Cellular Networks
This paper analyses the potential of utilising small unmanned-aerial-vehicles
(SUAV) as wireless relays for assisting cellular network performance. Whilst
high altitude wireless relays have been investigated over the past 2 decades,
the new class of low cost SUAVs offers new possibilities for addressing local
traffic imbalances and providing emergency coverage.We present field-test
results from an SUAV test-bed in both urban and rural environments. The results
show that trough-to-peak throughput improvements can be achieved for users in
poor coverage zones. Furthermore, the paper reinforces the experimental study
with large-scale network analysis using both stochastic geometry and multi-cell
simulation results.Comment: conferenc
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