2,939 research outputs found
Cooperative Local Caching under Heterogeneous File Preferences
Local caching is an effective scheme for leveraging the memory of the mobile
terminal (MT) and short range communications to save the bandwidth usage and
reduce the download delay in the cellular communication system. Specifically,
the MTs first cache in their local memories in off-peak hours and then exchange
the requested files with each other in the vicinity during peak hours. However,
prior works largely overlook MTs' heterogeneity in file preferences and their
selfish behaviours. In this paper, we practically categorize the MTs into
different interest groups according to the MTs' preferences. Each group of MTs
aims to increase the probability of successful file discovery from the
neighbouring MTs (from the same or different groups). Hence, we define the
groups' utilities as the probability of successfully discovering the file in
the neighbouring MTs, which should be maximized by deciding the caching
strategies of different groups. By modelling MTs' mobilities as homogeneous
Poisson point processes (HPPPs), we analytically characterize MTs' utilities in
closed-form. We first consider the fully cooperative case where a centralizer
helps all groups to make caching decisions. We formulate the problem as a
weighted-sum utility maximization problem, through which the maximum utility
trade-offs of different groups are characterized. Next, we study two benchmark
cases under selfish caching, namely, partial and no cooperation, with and
without inter-group file sharing, respectively. The optimal caching
distributions for these two cases are derived. Finally, numerical examples are
presented to compare the utilities under different cases and show the
effectiveness of the fully cooperative local caching compared to the two
benchmark cases
Cooperative Caching and Transmission Design in Cluster-Centric Small Cell Networks
Wireless content caching in small cell networks (SCNs) has recently been
considered as an efficient way to reduce the traffic and the energy consumption
of the backhaul in emerging heterogeneous cellular networks (HetNets). In this
paper, we consider a cluster-centric SCN with combined design of cooperative
caching and transmission policy. Small base stations (SBSs) are grouped into
disjoint clusters, in which in-cluster cache space is utilized as an entity. We
propose a combined caching scheme where part of the available cache space is
reserved for caching the most popular content in every SBS, while the remaining
is used for cooperatively caching different partitions of the less popular
content in different SBSs, as a means to increase local content diversity.
Depending on the availability and placement of the requested content,
coordinated multipoint (CoMP) technique with either joint transmission (JT) or
parallel transmission (PT) is used to deliver content to the served user. Using
Poisson point process (PPP) for the SBS location distribution and a hexagonal
grid model for the clusters, we provide analytical results on the successful
content delivery probability of both transmission schemes for a user located at
the cluster center. Our analysis shows an inherent tradeoff between
transmission diversity and content diversity in our combined
caching-transmission design. We also study optimal cache space assignment for
two objective functions: maximization of the cache service performance and the
energy efficiency. Simulation results show that the proposed scheme achieves
performance gain by leveraging cache-level and signal-level cooperation and
adapting to the network environment and user QoS requirements.Comment: 13 pages, 10 figures, submitted for possible journal publicatio
Simplifying Wireless Social Caching
Social groups give the opportunity for a new form of caching. In this paper,
we investigate how a social group of users can jointly optimize bandwidth
usage, by each caching parts of the data demand, and then opportunistically
share these parts among themselves upon meeting. We formulate this problem as a
Linear Program (LP) with exponential complexity. Based on the optimal solution,
we propose a simple heuristic inspired by the bipartite set-cover problem that
operates in polynomial time. Furthermore, we prove a worst case gap between the
heuristic and the LP solutions. Finally, we assess the performance of our
algorithm using real-world mobility traces from the MIT Reality Mining project
dataset and two mobility traces that were synthesized using the SWIM model. Our
heuristic performs closely to the optimal in most cases, showing a better
performance with respect to alternative solutions.Comment: Parts of this work were accepted for publication in ISIT 2016. A
complete version is submitted to Transactions on Mobile Computin
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