9 research outputs found
Optimal stochastic signaling for power-constrained binary communications systems
Cataloged from PDF version of article.Optimal stochastic signaling is studied under second
and fourth moment constraints for the detection of scalar-valued
binary signals in additive noise channels. Sufficient conditions
are obtained to specify when the use of stochastic signals
instead of deterministic ones can or cannot improve the error
performance of a given binary communications system. Also,
statistical characterization of optimal signals is presented, and it
is shown that an optimal stochastic signal can be represented by a
randomization of at most three different signal levels. In addition,
the power constraints achieved by optimal stochastic signals are
specified under various conditions. Furthermore, two approaches
for solving the optimal stochastic signaling problem are proposed;
one based on particle swarm optimization (PSO) and the other
based on convex relaxation of the original optimization problem.
Finally, simulations are performed to investigate the theoretical
results, and extensions of the results to -ary communications
systems and to other criteria than the average probability of
error are discussed
Noise Enhanced Hypothesis-Testing in the Restricted Bayesian Framework
Cataloged from PDF version of article.Performance of some suboptimal detectors can be enhanced by adding independent noise to their observations. In this paper, the effects of additive noise are investigated according to the restricted Bayes criterion, which provides a generalization of the Bayes and minimax criteria. Based on a generic M-ary composite hypothesis-testing formulation, the optimal probability distribution of additive noise is investigated. Also, sufficient conditions under which the performance of a detector can or cannot be improved via additive noise are derived. In addition, simple hypothesis-testing problems are studied in more detail, and additional improvability conditions that are specific to simple hypotheses are obtained. Furthermore, the optimal probability distribution of the additive noise is shown to include at most mass points in a simple M-ary hypothesis-testing problem under certain conditions. Then, global optimization, analytical and convex relaxation approaches are considered to obtain the optimal noise distribution. Finally, detection examples are presented to investigate the theoretical results
Stochastic resonance in binary composite hypothesis-testing problems in the Neyman-Pearson framework
Performance of some suboptimal detectors can be enhanced by adding independent noise to their inputs via the stochastic resonance (SR) effect. In this paper, the effects of SR are studied for binary composite hypothesis-testing problems. A Neyman-Pearson framework is considered, and the maximization of detection performance under a constraint on the maximum probability of false-alarm is studied. The detection performance is quantified in terms of the sum, the minimum, and the maximum of the detection probabilities corresponding to possible parameter values under the alternative hypothesis. Sufficient conditions under which detection performance can or cannot be improved are derived for each case. Also, statistical characterization of optimal additive noise is provided, and the resulting false-alarm probabilities and bounds on detection performance are investigated. In addition, optimization theoretic approaches to obtaining the probability distribution of optimal additive noise are discussed. Finally, a detection example is presented to investigate the theoretical results. © 2012 Elsevier Inc. All rights reserved
Development of new array signal processing techniques using swarm intelligence
Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 2010.Thesis (Ph. D.) -- Bilkent University, 2010.Includes bibliographical references leaves 144-158.In this thesis, novel array signal processing techniques are proposed for identifi-
cation of multipath communication channels based on cross ambiguity function
(CAF) calculation, swarm intelligence and compressed sensing (CS) theory. First
technique detects the presence of multipath components by integrating CAFs of
each antenna output in the array and iteratively estimates direction-of-arrivals
(DOAs), time delays and Doppler shifts of a known waveform. Second technique
called particle swarm optimization-cross ambiguity function (PSO-CAF) makes
use of the CAF calculation to transform the received antenna array outputs to
delay-Doppler domain for efficient exploitation of the delay-Doppler diversity of
the multipath components. Clusters of multipath components are identified by
using a simple amplitude thresholding in the delay-Doppler domain. PSO is
used to estimate parameters of the multipath components in each cluster. Third
proposed technique combines CS theory, swarm intelligence and CAF computation.
Performance of standard CS formulations based on discretization of the
multipath channel parameter space degrade significantly when the actual channel
parameters deviate from the assumed discrete set of values. To alleviate this
“off-grid”problem, a novel technique by making use of the PSO, that can also be
used in applications other than the multipath channel identification is proposed.
Performances of the proposed techniques are verified both on sythetic and real
data.Güldoğan, Mehmet BurakPh.D
Stochastic signaling for power constrained communication systems
Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Science of Bilkent University, 2011.Thesis (Master's) -- Bilkent University, 2011.Includes bibliographical references leaves 93-97.In this thesis, optimal stochastic signaling problem is studied for power constrained
communications systems. In the first part, optimal stochastic signaling
problem is investigated for binary communications systems under second and
fourth moment constraints for any given detector structure and noise probability
distribution. It is shown that an optimal signal can be represented by randomization
among at most three signal levels for each symbol. Next, stochastic signaling
problem is studied in the presence of an average power constraint instead of second
and fourth moment constraints. It is shown that an optimal signal can be
represented by randomization between at most two signal levels for each symbol
in this case. For both scenarios, sufficient conditions are obtained to determine
the improvability and nonimprovability of conventional deterministic signaling
via stochastic signaling. In the second part of the thesis, the joint design of
optimal signals and optimal detector is studied for binary communications systems
under average power constraints in the presence of additive non-Gaussian
noise. It is shown that the optimal solution involves randomization between at
most two signal levels and the use of the corresponding maximum a posteriori
probability (MAP) detector. In the last part of the thesis, stochastic signaling
is investigated for power-constrained scalar valued binary communications systems
in the presence of uncertainties in channel state information (CSI). First,
stochastic signaling is performed based on the available imperfect channel coef-
ficient at the transmitter to examine the effects of imperfect CSI. The sufficient
conditions are derived for improvability and nonimprovability of deterministic
signaling via stochastic signaling in the presence of CSI uncertainty. Then, two
different stochastic signaling strategies, namely, robust stochastic signaling and
stochastic signaling with averaging, are proposed for designing stochastic signals
under CSI uncertainty. For the robust stochastic signaling problem, sufficient
conditions are derived to obtain an equivalent form which is simpler to solve.
In addition, it is shown that optimal signals for each symbol can be written as
randomization between at most two signal levels for stochastic signaling using
imperfect channel coefficient and stochastic signaling with averaging as well as
for robust stochastic signaling under certain conditions. The solutions of the
optimal stochastic signaling problems are obtained by using global optimization
techniques, specifically, Particle Swarm Optimization (PSO), and by employing
convex relaxation approaches. Numerical examples are presented to illustrate
the theoretical results at the end of each part.Göken, ÇağrıM.S
Alternative approaches and noise benefits in hypothesis-testing problems in the presence of partial information
Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 2011.Thesis (Ph. D.) -- Bilkent University, 2011.Includes bibliographical references leaves 153-164.Performance of some suboptimal detectors can be enhanced by adding independent
noise to their observations. In the first part of the dissertation, the effects
of additive noise are studied according to the restricted Bayes criterion, which
provides a generalization of the Bayes and minimax criteria. Based on a generic
M-ary composite hypothesis-testing formulation, the optimal probability distribution
of additive noise is investigated. Also, sufficient conditions under which
the performance of a detector can or cannot be improved via additive noise are
derived. In addition, simple hypothesis-testing problems are studied in more
detail, and additional improvability conditions that are specific to simple hypotheses
are obtained. Furthermore, the optimal probability distribution of the
additive noise is shown to include at most M mass points in a simple M-ary
hypothesis-testing problem under certain conditions. Then, global optimization,
analytical and convex relaxation approaches are considered to obtain the optimal
noise distribution. Finally, detection examples are presented to investigate the
theoretical results.
In the second part of the dissertation, the effects of additive noise are studied
for M-ary composite hypothesis-testing problems in the presence of partial
prior information. Optimal additive noise is obtained according to two criteria,
which assume a uniform distribution (Criterion 1) or the least-favorable distribution
(Criterion 2) for the unknown priors. The statistical characterization of
the optimal noise is obtained for each criterion. Specifically, it is shown that the
optimal noise can be represented by a constant signal level or by a randomization
of a finite number of signal levels according to Criterion 1 and Criterion 2,
respectively. In addition, the cases of unknown parameter distributions under
some composite hypotheses are considered, and upper bounds on the risks are
obtained. Finally, a detection example is provided to illustrate the theoretical
results.
In the third part of the dissertation, the effects of additive noise are studied
for binary composite hypothesis-testing problems. A Neyman-Pearson (NP)
framework is considered, and the maximization of detection performance under a
constraint on the maximum probability of false-alarm is studied. The detection
performance is quantified in terms of the sum, the minimum and the maximum of
the detection probabilities corresponding to possible parameter values under the
alternative hypothesis. Sufficient conditions under which detection performance
can or cannot be improved are derived for each case. Also, statistical characterization
of optimal additive noise is provided, and the resulting false-alarm
probabilities and bounds on detection performance are investigated. In addition,
optimization theoretic approaches for obtaining the probability distribution of
optimal additive noise are discussed. Finally, a detection example is presented
to investigate the theoretical results.
Finally, the restricted NP approach is studied for composite hypothesistesting
problems in the presence of uncertainty in the prior probability distribution
under the alternative hypothesis. A restricted NP decision rule aims to
maximize the average detection probability under the constraints on the worstcase
detection and false-alarm probabilities, and adjusts the constraint on the
worst-case detection probability according to the amount of uncertainty in the
prior probability distribution. Optimal decision rules according to the restricted
NP criterion are investigated, and an algorithm is provided to calculate the optimal
restricted NP decision rule. In addition, it is observed that the average
detection probability is a strictly decreasing and concave function of the constraint
on the minimum detection probability. Finally, a detection example is
presented, and extensions to more generic scenarios are discussed.Bayram, SuatPh.D
Abstracts on Radio Direction Finding (1899 - 1995)
The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography).
Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM.
The contents of these files are:
1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format];
2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format];
3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion
Performance Analysis For Wireless G (IEEE 802.11 G) And Wireless N (IEEE 802.11 N) In Outdoor Environment
This paper described an analysis the different capabilities and limitation of both IEEE technologies that has been utilized for data transmission directed to mobile device. In this work, we have compared an IEEE 802.11/g/n outdoor environment to know what technology is better. the comparison consider on coverage area (mobility), through put and measuring the interferences. The work presented here is to help the researchers to select the best technology depending of their deploying case, and investigate the best variant for outdoor. The tool used is Iperf software which is to measure the data transmission performance of IEEE 802.11n and IEEE 802.11g
Performance analysis for wireless G (IEEE 802.11G) and wireless N (IEEE 802.11N) in outdoor environment
This paper described an analysis the different
capabilities and limitation of both IEEE technologies that has been utilized for data transmission directed to mobile device. In this work, we have compared an IEEE 802.11/g/n outdoor environment to know what technology is better. The comparison consider on coverage area (mobility), throughput and measuring the interferences. The work presented here is to help the researchers to select the best technology depending of their deploying case, and investigate the best variant for outdoor. The tool used is Iperf software which is to measure the data transmission performance of IEEE 802.11n and IEEE 802.11g