191 research outputs found
Diversity-Multiplexing Tradeoff of Asynchronous Cooperative Diversity in Wireless Networks
Synchronization of relay nodes is an important and critical issue in
exploiting cooperative diversity in wireless networks. In this paper, two
asynchronous cooperative diversity schemes are proposed, namely, distributed
delay diversity and asynchronous space-time coded cooperative diversity
schemes. In terms of the overall diversity-multiplexing (DM) tradeoff function,
we show that the proposed independent coding based distributed delay diversity
and asynchronous space-time coded cooperative diversity schemes achieve the
same performance as the synchronous space-time coded approach which requires an
accurate symbol-level timing synchronization to ensure signals arriving at the
destination from different relay nodes are perfectly synchronized. This
demonstrates diversity order is maintained even at the presence of asynchronism
between relay node. Moreover, when all relay nodes succeed in decoding the
source information, the asynchronous space-time coded approach is capable of
achieving better DM-tradeoff than synchronous schemes and performs equivalently
to transmitting information through a parallel fading channel as far as the
DM-tradeoff is concerned. Our results suggest the benefits of fully exploiting
the space-time degrees of freedom in multiple antenna systems by employing
asynchronous space-time codes even in a frequency flat fading channel. In
addition, it is shown asynchronous space-time coded systems are able to achieve
higher mutual information than synchronous space-time coded systems for any
finite signal-to-noise-ratio (SNR) when properly selected baseband waveforms
are employed
Coupling the reduced-order model and the generative model for an importance sampling estimator
In this work, we develop an importance sampling estimator by coupling the
reduced-order model and the generative model in a problem setting of
uncertainty quantification. The target is to estimate the probability that the
quantity of interest (QoI) in a complex system is beyond a given threshold. To
avoid the prohibitive cost of sampling a large scale system, the reduced-order
model is usually considered for a trade-off between efficiency and accuracy.
However, the Monte Carlo estimator given by the reduced-order model is biased
due to the error from dimension reduction. To correct the bias, we still need
to sample the fine model. An effective technique to reduce the variance
reduction is importance sampling, where we employ the generative model to
estimate the distribution of the data from the reduced-order model and use it
for the change of measure in the importance sampling estimator. To compensate
the approximation errors of the reduced-order model, more data that induce a
slightly smaller QoI than the threshold need to be included into the training
set. Although the amount of these data can be controlled by a posterior error
estimate, redundant data, which may outnumber the effective data, will be kept
due to the epistemic uncertainty. To deal with this issue, we introduce a
weighted empirical distribution to process the data from the reduced-order
model. The generative model is then trained by minimizing the cross entropy
between it and the weighted empirical distribution. We also introduce a penalty
term into the objective function to deal with the overfitting for more
robustness. Numerical results are presented to demonstrate the effectiveness of
the proposed methodology
Topological and Algebraic Properties of Chernoff Information between Gaussian Graphs
In this paper, we want to find out the determining factors of Chernoff
information in distinguishing a set of Gaussian graphs. We find that Chernoff
information of two Gaussian graphs can be determined by the generalized
eigenvalues of their covariance matrices. We find that the unit generalized
eigenvalue doesn't affect Chernoff information and its corresponding dimension
doesn't provide information for classification purpose. In addition, we can
provide a partial ordering using Chernoff information between a series of
Gaussian trees connected by independent grafting operations. With the
relationship between generalized eigenvalues and Chernoff information, we can
do optimal linear dimension reduction with least loss of information for
classification.Comment: Submitted to Allerton2018, and this version contains proofs of the
propositions in the pape
Synthesis of Gaussian Trees with Correlation Sign Ambiguity: An Information Theoretic Approach
In latent Gaussian trees the pairwise correlation signs between the variables
are intrinsically unrecoverable. Such information is vital since it completely
determines the direction in which two variables are associated. In this work,
we resort to information theoretical approaches to achieve two fundamental
goals: First, we quantify the amount of information loss due to unrecoverable
sign information. Second, we show the importance of such information in
determining the maximum achievable rate region, in which the observed output
vector can be synthesized, given its probability density function. In
particular, we model the graphical model as a communication channel and propose
a new layered encoding framework to synthesize observed data using upper layer
Gaussian inputs and independent Bernoulli correlation sign inputs from each
layer. We find the achievable rate region for the rate tuples of multi-layer
latent Gaussian messages to synthesize the desired observables.Comment: 14 pages, 9 figures, part of this work is submitted to Allerton 2016
conference, UIUC, IL, US
Enhancement of Secrecy of Block Ciphered Systems by Deliberate Noise
This paper considers the problem of end-end security enhancement by resorting
to deliberate noise injected in ciphertexts. The main goal is to generate a
degraded wiretap channel in application layer over which Wyner-type secrecy
encoding is invoked to deliver additional secure information. More
specifically, we study secrecy enhancement of DES block cipher working in
cipher feedback model (CFB) when adjustable and intentional noise is introduced
into encrypted data in application layer. A verification strategy in exhaustive
search step of linear attack is designed to allow Eve to mount a successful
attack in the noisy environment. Thus, a controllable wiretap channel is
created over multiple frames by taking advantage of errors in Eve's
cryptanalysis, whose secrecy capacity is found for the case of known channel
states at receivers. As a result, additional secure information can be
delivered by performing Wyner type secrecy encoding over super-frames ahead of
encryption, namely, our proposed secrecy encoding-then-encryption scheme. These
secrecy bits could be taken as symmetric keys for upcoming frames. Numerical
results indicate that a sufficiently large secrecy rate can be achieved by
selective noise addition.Comment: 11 pages, 8 figures, journa
Asymptotic Error Free Partitioning over Noisy Boolean Multiaccess Channels
In this paper, we consider the problem of partitioning active users in a
manner that facilitates multi-access without collision. The setting is of a
noisy, synchronous, Boolean, multi-access channel where active users (out
of a total of users) seek to access. A solution to the partition problem
places each of the users in one of groups (or blocks) such that no two
active nodes are in the same block. We consider a simple, but non-trivial and
illustrative case of active users and study the number of steps used
to solve the partition problem. By random coding and a suboptimal decoding
scheme, we show that for any , where and
are positive constants (independent of ), and can be
arbitrary small, the partition problem can be solved with error probability
, for large . Under the same scheme, we also bound from
the other direction, establishing that, for any ,
the error probability for large ; again and
are constants and can be arbitrarily small. These bounds on the number
of steps are lower than the tight achievable lower-bound in terms of for group testing (in which all active users are identified,
rather than just partitioned). Thus, partitioning may prove to be a more
efficient approach for multi-access than group testing.Comment: This paper was submitted in June 2014 to IEEE Transactions on
Information Theory, and is under review no
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