12,993 research outputs found
Strong experimental guarantees in ultrafast quantum random number generation
We describe a methodology and standard of proof for experimental claims of
quantum random number generation (QRNG), analogous to well-established methods
from precision measurement. For appropriately constructed physical
implementations, lower bounds on the quantum contribution to the average
min-entropy can be derived from measurements on the QRNG output. Given these
bounds, randomness extractors allow generation of nearly perfect
"{\epsilon}-random" bit streams. An analysis of experimental uncertainties then
gives experimentally derived confidence levels on the {\epsilon} randomness of
these sequences. We demonstrate the methodology by application to
phase-diffusion QRNG, driven by spontaneous emission as a trusted randomness
source. All other factors, including classical phase noise, amplitude
fluctuations, digitization errors and correlations due to finite detection
bandwidth, are treated with paranoid caution, i.e., assuming the worst possible
behaviors consistent with observations. A data-constrained numerical
optimization of the distribution of untrusted parameters is used to lower bound
the average min-entropy. Under this paranoid analysis, the QRNG remains
efficient, generating at least 2.3 quantum random bits per symbol with 8-bit
digitization and at least 0.83 quantum random bits per symbol with binary
digitization, at a confidence level of 0.99993. The result demonstrates
ultrafast QRNG with strong experimental guarantees.Comment: 11 pages, 9 figure
Performance Analysis of Cognitive Radio Systems under QoS Constraints and Channel Uncertainty
In this paper, performance of cognitive transmission over time-selective flat
fading channels is studied under quality of service (QoS) constraints and
channel uncertainty. Cognitive secondary users (SUs) are assumed to initially
perform channel sensing to detect the activities of the primary users, and then
attempt to estimate the channel fading coefficients through training. Energy
detection is employed for channel sensing, and different minimum
mean-square-error (MMSE) estimation methods are considered for channel
estimation. In both channel sensing and estimation, erroneous decisions can be
made, and hence, channel uncertainty is not completely eliminated. In this
setting, performance is studied and interactions between channel sensing and
estimation are investigated.
Following the channel sensing and estimation tasks, SUs engage in data
transmission. Transmitter, being unaware of the channel fading coefficients, is
assumed to send the data at fixed power and rate levels that depend on the
channel sensing results. Under these assumptions, a state-transition model is
constructed by considering the reliability of the transmissions, channel
sensing decisions and their correctness, and the evolution of primary user
activity which is modeled as a two-state Markov process. In the data
transmission phase, an average power constraint on the secondary users is
considered to limit the interference to the primary users, and statistical
limitations on the buffer lengths are imposed to take into account the QoS
constraints of the secondary traffic. The maximum throughput under these
statistical QoS constraints is identified by finding the effective capacity of
the cognitive radio channel. Numerical results are provided for the power and
rate policies
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
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