202,146 research outputs found
Robust Beamforming for Security in MIMO Wiretap Channels with Imperfect CSI
In this paper, we investigate methods for reducing the likelihood that a
message transmitted between two multiantenna nodes is intercepted by an
undetected eavesdropper. In particular, we focus on the judicious transmission
of artificial interference to mask the desired signal at the time it is
broadcast. Unlike previous work that assumes some prior knowledge of the
eavesdropper's channel and focuses on maximizing secrecy capacity, we consider
the case where no information regarding the eavesdropper is available, and we
use signal-to-interference-plus-noise-ratio (SINR) as our performance metric.
Specifically, we focus on the problem of maximizing the amount of power
available to broadcast a jamming signal intended to hide the desired signal
from a potential eavesdropper, while maintaining a prespecified SINR at the
desired receiver. The jamming signal is designed to be orthogonal to the
information signal when it reaches the desired receiver, assuming both the
receiver and the eavesdropper employ optimal beamformers and possess exact
channel state information (CSI). In practice, the assumption of perfect CSI at
the transmitter is often difficult to justify. Therefore, we also study the
resulting performance degradation due to the presence of imperfect CSI, and we
present robust beamforming schemes that recover a large fraction of the
performance in the perfect CSI case. Numerical simulations verify our
analytical performance predictions, and illustrate the benefit of the robust
beamforming schemes.Comment: 10 pages, 5 figures; to appear, IEEE Transactions on Signal
Processing, 201
Culture and E-Learning: Automatic Detection of a Users’ Culture from Survey Data
Knowledge about the culture of a user is especially important for the design
of e-learning applications. In the experiment reported here, questionnaire
data was used to build machine learning models to automatically predict the
culture of a user. This work can be applied to automatic culture detection
and subsequently to the adaptation of user interfaces in e-learning
A Contextual Bandit Bake-off
Contextual bandit algorithms are essential for solving many real-world
interactive machine learning problems. Despite multiple recent successes on
statistically and computationally efficient methods, the practical behavior of
these algorithms is still poorly understood. We leverage the availability of
large numbers of supervised learning datasets to empirically evaluate
contextual bandit algorithms, focusing on practical methods that learn by
relying on optimization oracles from supervised learning. We find that a recent
method (Foster et al., 2018) using optimism under uncertainty works the best
overall. A surprisingly close second is a simple greedy baseline that only
explores implicitly through the diversity of contexts, followed by a variant of
Online Cover (Agarwal et al., 2014) which tends to be more conservative but
robust to problem specification by design. Along the way, we also evaluate
various components of contextual bandit algorithm design such as loss
estimators. Overall, this is a thorough study and review of contextual bandit
methodology
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