2,172 research outputs found
GreenDelivery: Proactive Content Caching and Push with Energy-Harvesting-based Small Cells
The explosive growth of mobile multimedia traffic calls for scalable wireless
access with high quality of service and low energy cost. Motivated by the
emerging energy harvesting communications, and the trend of caching multimedia
contents at the access edge and user terminals, we propose a paradigm-shift
framework, namely GreenDelivery, enabling efficient content delivery with
energy harvesting based small cells. To resolve the two-dimensional randomness
of energy harvesting and content request arrivals, proactive caching and push
are jointly optimized, with respect to the content popularity distribution and
battery states. We thus develop a novel way of understanding the interplay
between content and energy over time and space. Case studies are provided to
show the substantial reduction of macro BS activities, and thus the related
energy consumption from the power grid is reduced. Research issues of the
proposed GreenDelivery framework are also discussed.Comment: 15 pages, 5 figures, accepted by IEEE Communications Magazin
Self-Sustaining Caching Stations: Towards Cost-Effective 5G-Enabled Vehicular Networks
In this article, we investigate the cost-effective 5G-enabled vehicular
networks to support emerging vehicular applications, such as autonomous
driving, in-car infotainment and location-based road services. To this end,
self-sustaining caching stations (SCSs) are introduced to liberate on-road base
stations from the constraints of power lines and wired backhauls. Specifically,
the cache-enabled SCSs are powered by renewable energy and connected to core
networks through wireless backhauls, which can realize "drop-and-play"
deployment, green operation, and low-latency services. With SCSs integrated, a
5G-enabled heterogeneous vehicular networking architecture is further proposed,
where SCSs are deployed along roadside for traffic offloading while
conventional macro base stations (MBSs) provide ubiquitous coverage to
vehicles. In addition, a hierarchical network management framework is designed
to deal with high dynamics in vehicular traffic and renewable energy, where
content caching, energy management and traffic steering are jointly
investigated to optimize the service capability of SCSs with balanced power
demand and supply in different time scales. Case studies are provided to
illustrate SCS deployment and operation designs, and some open research issues
are also discussed.Comment: IEEE Communications Magazine, to appea
Mitigating Interference in Content Delivery Networks by Spatial Signal Alignment: The Approach of Shot-Noise Ratio
Multimedia content especially videos is expected to dominate data traffic in
next-generation mobile networks. Caching popular content at the network edge
has emerged to be a solution for low-latency content delivery. Compared with
the traditional wireless communication, content delivery has a key
characteristic that many signals coexisting in the air carry identical popular
content. They, however, can interfere with each other at a receiver if their
modulation-and-coding (MAC) schemes are adapted to individual channels
following the classic approach. To address this issue, we present a novel idea
of content adaptive MAC (CAMAC) where adapting MAC schemes to content ensures
that all signals carry identical content are encoded using an identical MAC
scheme, achieving spatial MAC alignment. Consequently, interference can be
harnessed as signals, to improve the reliability of wireless delivery. In the
remaining part of the paper, we focus on quantifying the gain CAMAC can bring
to a content-delivery network using a stochastic-geometry model. Specifically,
content helpers are distributed as a Poisson point process, each of which
transmits a file from a content database based on a given popularity
distribution. It is discovered that the successful content-delivery probability
is closely related to the distribution of the ratio of two independent shot
noise processes, named a shot-noise ratio. The distribution itself is an open
mathematical problem that we tackle in this work. Using stable-distribution
theory and tools from stochastic geometry, the distribution function is derived
in closed form. Extending the result in the context of content-delivery
networks with CAMAC yields the content-delivery probability in different closed
forms. In addition, the gain in the probability due to CAMAC is shown to grow
with the level of skewness in the content popularity distribution.Comment: 32 pages, to appear in IEEE Trans. on Wireless Communicatio
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
A joint scheduling and content caching scheme for energy harvesting access points with multicast
© 2017 IEEE. In this work, we investigate a system where users are served by an access point that is equipped with energy harvesting and caching mechanism. Focusing on the design of an efficient content delivery scheduling, we propose a joint scheduling and caching scheme. The scheduling problem is formulated as a Markov decision process and solved by an on-line learning algorithm. To deal with large state space, we apply the linear approximation method to the state-Action value functions, which significantly reduces the memory space for storing the function values. In addition, the preference learning is incorporated to speed up the convergence when dealing with the requests from users that have obvious content preferences. Simulation results confirm that the proposed scheme outperforms the baseline scheme in terms of convergence and system throughput, especially when the personal preference is concentrated to one or two contents
Cost-Effective Cache Deployment in Mobile Heterogeneous Networks
This paper investigates one of the fundamental issues in cache-enabled
heterogeneous networks (HetNets): how many cache instances should be deployed
at different base stations, in order to provide guaranteed service in a
cost-effective manner. Specifically, we consider two-tier HetNets with
hierarchical caching, where the most popular files are cached at small cell
base stations (SBSs) while the less popular ones are cached at macro base
stations (MBSs). For a given network cache deployment budget, the cache sizes
for MBSs and SBSs are optimized to maximize network capacity while satisfying
the file transmission rate requirements. As cache sizes of MBSs and SBSs affect
the traffic load distribution, inter-tier traffic steering is also employed for
load balancing. Based on stochastic geometry analysis, the optimal cache sizes
for MBSs and SBSs are obtained, which are threshold-based with respect to cache
budget in the networks constrained by SBS backhauls. Simulation results are
provided to evaluate the proposed schemes and demonstrate the applications in
cost-effective network deployment
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