1,302 research outputs found
Online Learning Models for Content Popularity Prediction In Wireless Edge Caching
Caching popular contents in advance is an important technique to achieve the
low latency requirement and to reduce the backhaul costs in future wireless
communications. Considering a network with base stations distributed as a
Poisson point process (PPP), optimal content placement caching probabilities
are derived for known popularity profile, which is unknown in practice. In this
paper, online prediction (OP) and online learning (OL) methods are presented
based on popularity prediction model (PPM) and Grassmannian prediction model
(GPM), to predict the content profile for future time slots for time-varying
popularities. In OP, the problem of finding the coefficients is modeled as a
constrained non-negative least squares (NNLS) problem which is solved with a
modified NNLS algorithm. In addition, these two models are compared with
log-request prediction model (RPM), information prediction model (IPM) and
average success probability (ASP) based model. Next, in OL methods for the
time-varying case, the cumulative mean squared error (MSE) is minimized and the
MSE regret is analyzed for each of the models. Moreover, for quasi-time varying
case where the popularity changes block-wise, KWIK (know what it knows)
learning method is modified for these models to improve the prediction MSE and
ASP performance. Simulation results show that for OP, PPM and GPM provides the
best ASP among these models, concluding that minimum mean squared error based
models do not necessarily result in optimal ASP. OL based models yield
approximately similar ASP and MSE, while for quasi-time varying case, KWIK
methods provide better performance, which has been verified with MovieLens
dataset.Comment: 9 figure, 29 page
On Optimal Geographical Caching in Heterogeneous Cellular Networks
In this work we investigate optimal geographical caching in heterogeneous
cellular networks where different types of base stations (BSs) have different
cache capacities. Users request files from a content library according to a
known probability distribution. The performance metric is the total hit
probability, which is the probability that a user at an arbitrary location in
the plane will find the content that it requires in one of the BSs that it is
covered by.
We consider the problem of optimally placing content in all BSs jointly. As
this problem is not convex, we provide a heuristic scheme by finding the
optimal placement policy for one type of base station conditioned on the
placement in all other types. We demonstrate that these individual optimization
problems are convex and we provide an analytical solution. As an illustration,
we find the optimal placement policy of the small base stations (SBSs)
depending on the placement policy of the macro base stations (MBSs). We show
how the hit probability evolves as the deployment density of the SBSs varies.
We show that the heuristic of placing the most popular content in the MBSs is
almost optimal after deploying the SBSs with optimal placement policies. Also,
for the SBSs no such heuristic can be used; the optimal placement is
significantly better than storing the most popular content. Finally, we show
that solving the individual problems to find the optimal placement policies for
different types of BSs iteratively, namely repeatedly updating the placement
policies, does not improve the performance.Comment: The article has 6 pages, 7 figures and is accepted to be presented at
IEEE Wireless Communications and Networking Conference (WCNC) 2017, 19 - 22
March 2017, San Francisco, CA, US
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