8,482 research outputs found
A Framework for Phasor Measurement Placement in Hybrid State Estimation via Gauss-Newton
In this paper, we study the placement of Phasor Measurement Units (PMU) for
enhancing hybrid state estimation via the traditional Gauss-Newton method,
which uses measurements from both PMU devices and Supervisory Control and Data
Acquisition (SCADA) systems. To compare the impact of PMU placements, we
introduce a useful metric which accounts for three important requirements in
power system state estimation: {\it convergence}, {\it observability} and {\it
performance} (COP). Our COP metric can be used to evaluate the estimation
performance and numerical stability of the state estimator, which is later used
to optimize the PMU locations. In particular, we cast the optimal placement
problem in a unified formulation as a semi-definite program (SDP) with integer
variables and constraints that guarantee observability in case of measurements
loss. Last but not least, we propose a relaxation scheme of the original
integer-constrained SDP with randomization techniques, which closely
approximates the optimum deployment. Simulations of the IEEE-30 and 118 systems
corroborate our analysis, showing that the proposed scheme improves the
convergence of the state estimator, while maintaining optimal asymptotic
performance.Comment: accepted to IEEE Trans. on Power System
Multicell Coordinated Beamforming with Rate Outage Constraint--Part I: Complexity Analysis
This paper studies the coordinated beamforming (CoBF) design in the
multiple-input single-output interference channel, assuming only channel
distribution information given a priori at the transmitters. The CoBF design is
formulated as an optimization problem that maximizes a predefined system
utility, e.g., the weighted sum rate or the weighted max-min-fairness (MMF)
rate, subject to constraints on the individual probability of rate outage and
power budget. While the problem is non-convex and appears difficult to handle
due to the intricate outage probability constraints, so far it is still unknown
if this outage constrained problem is computationally tractable. To answer
this, we conduct computational complexity analysis of the outage constrained
CoBF problem. Specifically, we show that the outage constrained CoBF problem
with the weighted sum rate utility is intrinsically difficult, i.e., NP-hard.
Moreover, the outage constrained CoBF problem with the weighted MMF rate
utility is also NP-hard except the case when all the transmitters are equipped
with single antenna. The presented analysis results confirm that efficient
approximation methods are indispensable to the outage constrained CoBF problem.Comment: submitted to IEEE Transactions on Signal Processin
A multi-task learning CNN for image steganalysis
Convolutional neural network (CNN) based image steganalysis are increasingly popular because of their superiority in accuracy. The most straightforward way to employ CNN for image steganalysis is to learn a CNN-based classifier to distinguish whether secret messages have been embedded into an image. However, it is difficult to learn such a classifier because of the weak stego signals and the limited useful information. To address this issue, in this paper, a multi-task learning CNN is proposed. In addition to the typical use of CNN, learning a CNN-based classifier for the whole image, our multi-task CNN is learned with an auxiliary task of the pixel binary classification, estimating whether each pixel in an image has been modified due to steganography. To the best of our knowledge, we are the first to employ CNN to perform the pixel-level classification of such type. Experimental results have justified the effectiveness and efficiency of the proposed multi-task learning CNN
Coordinated Multicasting with Opportunistic User Selection in Multicell Wireless Systems
Physical layer multicasting with opportunistic user selection (OUS) is
examined for multicell multi-antenna wireless systems. By adopting a two-layer
encoding scheme, a rate-adaptive channel code is applied in each fading block
to enable successful decoding by a chosen subset of users (which varies over
different blocks) and an application layer erasure code is employed across
multiple blocks to ensure that every user is able to recover the message after
decoding successfully in a sufficient number of blocks. The transmit signal and
code-rate in each block determine opportunistically the subset of users that
are able to successfully decode and can be chosen to maximize the long-term
multicast efficiency. The employment of OUS not only helps avoid
rate-limitations caused by the user with the worst channel, but also helps
coordinate interference among different cells and multicast groups. In this
work, efficient algorithms are proposed for the design of the transmit
covariance matrices, the physical layer code-rates, and the target user subsets
in each block. In the single group scenario, the system parameters are
determined by maximizing the group-rate, defined as the physical layer
code-rate times the fraction of users that can successfully decode in each
block. In the multi-group scenario, the system parameters are determined by
considering a group-rate balancing optimization problem, which is solved by a
successive convex approximation (SCA) approach. To further reduce the feedback
overhead, we also consider the case where only part of the users feed back
their channel vectors in each block and propose a design based on the balancing
of the expected group-rates. In addition to SCA, a sample average approximation
technique is also introduced to handle the probabilistic terms arising in this
problem. The effectiveness of the proposed schemes is demonstrated by computer
simulations.Comment: Accepted by IEEE Transactions on Signal Processin
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