1,024 research outputs found
Hypergraph-Based Analysis of Clustered Cooperative Beamforming with Application to Edge Caching
The evaluation of the performance of clustered cooperative beamforming in
cellular networks generally requires the solution of complex non-convex
optimization problems. In this letter, a framework based on a hypergraph
formalism is proposed that enables the derivation of a performance
characterization of clustered cooperative beamforming in terms of per-user
degrees of freedom (DoF) via the efficient solution of a coloring problem. An
emerging scenario in which clusters of cooperative base stations (BSs) arise is
given by cellular networks with edge caching. In fact, clusters of BSs that
share the same requested files can jointly beamform the corresponding encoded
signals. Based on this observation, the proposed framework is applied to obtain
quantitative insights into the optimal use of cache and backhaul resources in
cellular systems with edge caching. Numerical examples are provided to
illustrate the merits of the proposed framework.Comment: 10 pages, 5 figures, Submitte
Energy Efficiency in Cache Enabled Small Cell Networks With Adaptive User Clustering
Using a network of cache enabled small cells, traffic during peak hours can
be reduced considerably through proactively fetching the content that is most
probable to be requested. In this paper, we aim at exploring the impact of
proactive caching on an important metric for future generation networks,
namely, energy efficiency (EE). We argue that, exploiting the correlation in
user content popularity profiles in addition to the spatial repartitions of
users with comparable request patterns, can result in considerably improving
the achievable energy efficiency of the network. In this paper, the problem of
optimizing EE is decoupled into two related subproblems. The first one
addresses the issue of content popularity modeling. While most existing works
assume similar popularity profiles for all users in the network, we consider an
alternative caching framework in which, users are clustered according to their
content popularity profiles. In order to showcase the utility of the proposed
clustering scheme, we use a statistical model selection criterion, namely
Akaike information criterion (AIC). Using stochastic geometry, we derive a
closed-form expression of the achievable EE and we find the optimal active
small cell density vector that maximizes it. The second subproblem investigates
the impact of exploiting the spatial repartitions of users with comparable
request patterns. After considering a snapshot of the network, we formulate a
combinatorial optimization problem that enables to optimize content placement
such that the used transmission power is minimized. Numerical results show that
the clustering scheme enable to considerably improve the cache hit probability
and consequently the EE compared with an unclustered approach. Simulations also
show that the small base station allocation algorithm results in improving the
energy efficiency and hit probability.Comment: 30 pages, 5 figures, submitted to Transactions on Wireless
Communications (15-Dec-2016
On the Delay of Geographical Caching Methods in Two-Tiered Heterogeneous Networks
We consider a hierarchical network that consists of mobile users, a
two-tiered cellular network (namely small cells and macro cells) and central
routers, each of which follows a Poisson point process (PPP). In this scenario,
small cells with limited-capacity backhaul are able to cache content under a
given set of randomized caching policies and storage constraints. Moreover, we
consider three different content popularity models, namely fixed content
popularity, distance-dependent and load-dependent, in order to model the
spatio-temporal behavior of users' content request patterns. We derive
expressions for the average delay of users assuming perfect knowledge of
content popularity distributions and randomized caching policies. Although the
trend of the average delay for all three content popularity models is
essentially identical, our results show that the overall performance of
cached-enabled heterogeneous networks can be substantially improved, especially
under the load-dependent content popularity model.Comment: to be presented at IEEE SPAWC'2016, Edinburgh, U
Caching Improvement Using Adaptive User Clustering
In this article we explore one of the most promising technologies for 5G
wireless networks using an underlay small cell network, namely proactive
caching. Using the increase in storage technologies and through studying the
users behavior, peak traffic can be reduced through proactive caching of the
content that is most probable to be requested. We propose a new method, in
which, instead of caching the most popular content, the users within the
network are clustered according to their content popularity and the caching is
done accordingly. We present also a method for estimating the number of
clusters within the network based on the Akaike information criterion. We
analytically derive a closed form expression of the hit probability and we
propose an optimization problem in which the small base stations association
with clusters is optimized
Modeling and Analysis of Content Caching in Wireless Small Cell Networks
Network densification with small cell base stations is a promising solution
to satisfy future data traffic demands. However, increasing small cell base
station density alone does not ensure better users quality-of-experience and
incurs high operational expenditures. Therefore, content caching on different
network elements has been proposed as a mean of offloading he backhaul by
caching strategic contents at the network edge, thereby reducing latency. In
this paper, we investigate cache-enabled small cells in which we model and
characterize the outage probability, defined as the probability of not
satisfying users requests over a given coverage area. We analytically derive a
closed form expression of the outage probability as a function of
signal-to-interference ratio, cache size, small cell base station density and
threshold distance. By assuming the distribution of base stations as a Poisson
point process, we derive the probability of finding a specific content within a
threshold distance and the optimal small cell base station density that
achieves a given target cache hit probability. Furthermore, simulation results
are performed to validate the analytical model.Comment: accepted for publication, IEEE ISWCS 201
Speeding up Future Video Distribution via Channel-Aware Caching-Aided Coded Multicast
Future Internet usage will be dominated by the consumption of a rich variety
of online multimedia services accessed from an exponentially growing number of
multimedia capable mobile devices. As such, future Internet designs will be
challenged to provide solutions that can deliver bandwidth-intensive,
delay-sensitive, on-demand video-based services over increasingly crowded,
bandwidth-limited wireless access networks. One of the main reasons for the
bandwidth stress facing wireless network operators is the difficulty to exploit
the multicast nature of the wireless medium when wireless users or access
points rarely experience the same channel conditions or access the same content
at the same time. In this paper, we present and analyze a novel wireless video
delivery paradigm based on the combined use of channel-aware caching and coded
multicasting that allows simultaneously serving multiple cache-enabled
receivers that may be requesting different content and experiencing different
channel conditions. To this end, we reformulate the caching-aided coded
multicast problem as a joint source-channel coding problem and design an
achievable scheme that preserves the cache-enabled multiplicative throughput
gains of the error-free scenario,by guaranteeing per-receiver rates unaffected
by the presence of receivers with worse channel conditions.Comment: 11 pages,6 figures,to appear in IEEE JSAC Special Issue on Video
Distribution over Future Interne
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