121 research outputs found
On the evolution of infrastructure sharing in mobile networks: A survey
ABSTRACT: Infrastructure sharing for mobile networks has been a prolific research topic for more than three decades now. The key driver for Mobile Network Operators to share their network infrastructure is cost reduction. Spectrum sharing is often studied alongside infrastructure sharing although on its own it is a vast research topic outside the scope of this survey. Instead, in this survey we aim to provide a complete picture of infrastructure sharing both over time and in terms of research branches that have stemmed from it such as performance evaluation, resource management etc. We also put an emphasis on the relation between infrastructure sharing and the decoupling of infrastructure from services, wireless network virtualization and multi-tenancy in 5G networks. Such a relation reflects the evolution of infrastructure sharing over time and how it has become a commercial reality in the context of 5
A Machine Learning framework for Sleeping Cell Detection in a Smart-city IoT Telecommunications Infrastructure
The smooth operation of largely deployed Internet of Things (IoT)
applications will depend on, among other things, effective infrastructure
failure detection. Access failures in wireless network Base Stations (BSs)
produce a phenomenon called "sleeping cells", which can render a cell catatonic
without triggering any alarms or provoking immediate effects on cell
performance, making them difficult to discover. To detect this kind of failure,
we propose a Machine Learning (ML) framework based on the use of Key
Performance Indicator (KPI) statistics from the BS under study, as well as
those of the neighboring BSs with propensity to have their performance affected
by the failure. A simple way to define neighbors is to use adjacency in Voronoi
diagrams. In this paper, we propose a much more realistic approach based on the
nature of radio-propagation and the way devices choose the BS to which they
send access requests. We gather data from large-scale simulators that use real
location data for BSs and IoT devices and pose the detection problem as a
supervised binary classification problem. We measure the effects on the
detection performance by the size of time aggregations of the data, the level
of traffic and the parameters of the neighborhood definition. The Extra Trees
and Naive Bayes classifiers achieve Receiver Operating Characteristic (ROC)
Area Under the Curve (AUC) scores of 0.996 and 0.993, respectively, with False
Positive Rate (FPR) under 5 %. The proposed framework holds potential for other
pattern recognition tasks in smart-city wireless infrastructures, that would
enable the monitoring, prediction and improvement of the Quality of Service
(QoS) experienced by IoT applications.Comment: Submitted to the IEEE Access Journa
Robust Cooperative Spectrum Sensing Scheduling Optimization in Multi-Channel Dynamic Spectrum Access Networks
Dynamic spectrum access (DSA) enables secondary networks to find and
efficiently exploit spectrum opportunities. A key factor to design a DSA
network is the spectrum sensing algorithms for multiple channels with multiple
users. Multi-user cooperative channel sensing reduces the sensing time, and
thus it increases transmission throughput. However, in a multi-channel system,
the problem becomes more complex since the benefits of assigning users to sense
channels in parallel must also be considered. A sensing schedule, indicating to
each user the channel that it should sense at different sensing moments, must
be thus created to optimize system performance. In this paper, we formulate the
general sensing scheduling optimization problem and then propose several
sensing strategies to schedule the users according to network parameters with
homogeneous sensors. Later on we extend the results to heterogeneous sensors
and propose a robust scheduling design when we have traffic and channel
uncertainty. We propose three sensing strategies, and, within each one of them,
several solutions, striking a balance between throughput performance and
computational complexity, are proposed. In addition, we show that a sequential
channel sensing strategy is the one to be preferred when the sensing time is
small, the number of channels is large, and the number of users is small. For
all the other cases, a parallel channel sensing strategy is recommended in
terms of throughput performance. We also show that a proposed hybrid
sequential-parallel channel sensing strategy achieves the best performance in
all scenarios at the cost of extra memory and computation complexity
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