143,520 research outputs found
MobiCacher: Mobility-Aware Content Caching in Small-Cell Networks
Small-cell networks have been proposed to meet the demand of ever growing
mobile data traffic. One of the prominent challenges faced by small-cell
networks is the lack of sufficient backhaul capacity to connect small-cell base
stations (small-BSs) to the core network. We exploit the effective application
layer semantics of both spatial and temporal locality to reduce the backhaul
traffic. Specifically, small-BSs are equipped with storage facility to cache
contents requested by users. As the {\em cache hit ratio} increases, most of
the users' requests can be satisfied locally without incurring traffic over the
backhaul. To make informed caching decisions, the mobility patterns of users
must be carefully considered as users might frequently migrate from one small
cell to another. We study the issue of mobility-aware content caching, which is
formulated into an optimization problem with the objective to maximize the
caching utility. As the problem is NP-complete, we develop a polynomial-time
heuristic solution termed {\em MobiCacher} with bounded approximation ratio. We
also conduct trace-based simulations to evaluate the performance of {\em
MobiCacher}, which show that {\em MobiCacher} yields better caching utility
than existing solutions.Comment: Accepted by Globecom 201
Worst-Case Linear Discriminant Analysis as Scalable Semidefinite Feasibility Problems
In this paper, we propose an efficient semidefinite programming (SDP)
approach to worst-case linear discriminant analysis (WLDA). Compared with the
traditional LDA, WLDA considers the dimensionality reduction problem from the
worst-case viewpoint, which is in general more robust for classification.
However, the original problem of WLDA is non-convex and difficult to optimize.
In this paper, we reformulate the optimization problem of WLDA into a sequence
of semidefinite feasibility problems. To efficiently solve the semidefinite
feasibility problems, we design a new scalable optimization method with
quasi-Newton methods and eigen-decomposition being the core components. The
proposed method is orders of magnitude faster than standard interior-point
based SDP solvers.
Experiments on a variety of classification problems demonstrate that our
approach achieves better performance than standard LDA. Our method is also much
faster and more scalable than standard interior-point SDP solvers based WLDA.
The computational complexity for an SDP with constraints and matrices of
size by is roughly reduced from to
( in our case).Comment: 14 page
On methods to determine bounds on the Q-factor for a given directivity
This paper revisit and extend the interesting case of bounds on the Q-factor
for a given directivity for a small antenna of arbitrary shape. A higher
directivity in a small antenna is closely connected with a narrow impedance
bandwidth. The relation between bandwidth and a desired directivity is still
not fully understood, not even for small antennas. Initial investigations in
this direction has related the radius of a circumscribing sphere to the
directivity, and bounds on the Q-factor has also been derived for a partial
directivity in a given direction. In this paper we derive lower bounds on the
Q-factor for a total desired directivity for an arbitrarily shaped antenna in a
given direction as a convex problem using semi-definite relaxation techniques
(SDR). We also show that the relaxed solution is also a solution of the
original problem of determining the lower Q-factor bound for a total desired
directivity.
SDR can also be used to relax a class of other interesting non-convex
constraints in antenna optimization such as tuning, losses, front-to-back
ratio. We compare two different new methods to determine the lowest Q-factor
for arbitrary shaped antennas for a given total directivity. We also compare
our results with full EM-simulations of a parasitic element antenna with high
directivity.Comment: Correct some minor typos in the previous versio
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