2 research outputs found
Modeling and Performance Analysis of Clustered Device-to-Device Networks
Device-to-device (D2D) communication enables direct communication between
proximate devices thereby improving the overall spectrum utilization and
offloading traffic from cellular networks. This paper develops a new spatial
model for D2D networks in which the device locations are modeled as a Poisson
cluster process. Using this model, we study the performance of a typical D2D
receiver in terms of coverage probability under two realistic content
availability setups: (i) content of interest for a typical device is available
at a device chosen uniformly at random from the same cluster, which we term
uniform content availability, and (ii) content of interest is available at the
closest device from the typical device inside the same cluster, which
we term -closest content availability. Using these coverage probability
results, we also characterize the area spectral efficiency (ASE) of the whole
network for the two setups. A key intermediate step in this analysis is the
derivation of the distributions of distances from a typical device to both the
intra- and inter-cluster devices. Our analysis reveals that an optimum number
of D2D transmitters must be simultaneously activated per cluster in order to
maximize ASE. This can be interpreted as the classical tradeoff between more
aggressive frequency reuse and higher interference power. The optimum number of
simultaneously transmitting devices and the resulting ASE increase as the
content is made available closer to the receivers. Our analysis also quantifies
the best and worst case performance of clustered D2D networks both in terms of
coverage and ASE.Comment: 34 double-spaced pages, 10 figures. Submitted to IEEE Transactions on
Wireless Communication
Fundamentals of Cluster-Centric Content Placement in Cache-Enabled Device-to-Device Networks
This paper develops a comprehensive analytical framework with foundations in
stochastic geometry to characterize the performance of cluster-centric content
placement in a cache-enabled device-to-device (D2D) network. Different from
device-centric content placement, cluster-centric placement focuses on placing
content in each cluster such that the collective performance of all the devices
in each cluster is optimized. Modeling the locations of the devices by a
Poisson cluster process, we define and analyze the performance for three
general cases: (i)-Tx case: receiver of interest is chosen uniformly at
random in a cluster and its content of interest is available at the
closest device to the cluster center, (ii) -Rx case: receiver of interest
is the closest device to the cluster center and its content of
interest is available at a device chosen uniformly at random from the same
cluster, and (iii) baseline case: the receiver of interest is chosen uniformly
at random in a cluster and its content of interest is available at a device
chosen independently and uniformly at random from the same cluster. Easy-to-use
expressions for the key performance metrics, such as coverage probability and
area spectral efficiency (ASE) of the whole network, are derived for all three
cases. Our analysis concretely demonstrates significant improvement in the
network performance when the device on which content is cached or device
requesting content from cache is biased to lie closer to the cluster center
compared to baseline case. Based on this insight, we develop and analyze a new
generative model for cluster-centric D2D networks that allows to study the
effect of intra-cluster interfering devices that are more likely to lie closer
to the cluster center.Comment: 16 pages, 10 figures. Submitted to IEEE Transactions on
Communication