11,700 research outputs found
Gaussian State Amplification with Noisy State Observations
The problem of simultaneous message transmission and state amplification in a
Gaussian channel with additive Gaussian state is studied when the sender has
imperfect noncausal knowledge of the state sequence. Inner and outer bounds to
the rate--state-distortion region are provided. The coding scheme underlying
the inner bound combines analog signaling and Gelfand-Pinsker coding, where the
latter deviates from the operating point of Costa's dirty paper coding.Comment: 5 pages, 4 figures; submitted to IEEE International Symposium on
Information Theory (ISIT 2013
Power Allocation for Distributed BLUE Estimation with Full and Limited Feedback of CSI
This paper investigates the problem of adaptive power allocation for
distributed best linear unbiased estimation (BLUE) of a random parameter at the
fusion center (FC) of a wireless sensor network (WSN). An optimal
power-allocation scheme is proposed that minimizes the -norm of the vector
of local transmit powers, given a maximum variance for the BLUE estimator. This
scheme results in the increased lifetime of the WSN compared to similar
approaches that are based on the minimization of the sum of the local transmit
powers. The limitation of the proposed optimal power-allocation scheme is that
it requires the feedback of the instantaneous channel state information (CSI)
from the FC to local sensors, which is not practical in most applications of
large-scale WSNs. In this paper, a limited-feedback strategy is proposed that
eliminates this requirement by designing an optimal codebook for the FC using
the generalized Lloyd algorithm with modified distortion metrics. Each sensor
amplifies its analog noisy observation using a quantized version of its optimal
amplification gain, which is received by the FC and used to estimate the
unknown parameter.Comment: 6 pages, 3 figures, to appear at the IEEE Military Communications
Conference (MILCOM) 201
Limited-Feedback-Based Channel-Aware Power Allocation for Linear Distributed Estimation
This paper investigates the problem of distributed best linear unbiased
estimation (BLUE) of a random parameter at the fusion center (FC) of a wireless
sensor network (WSN). In particular, the application of limited-feedback
strategies for the optimal power allocation in distributed estimation is
studied. In order to find the BLUE estimator of the unknown parameter, the FC
combines spatially distributed, linearly processed, noisy observations of local
sensors received through orthogonal channels corrupted by fading and additive
Gaussian noise. Most optimal power-allocation schemes proposed in the
literature require the feedback of the exact instantaneous channel state
information from the FC to local sensors. This paper proposes a
limited-feedback strategy in which the FC designs an optimal codebook
containing the optimal power-allocation vectors, in an iterative offline
process, based on the generalized Lloyd algorithm with modified distortion
functions. Upon observing a realization of the channel vector, the FC finds the
closest codeword to its corresponding optimal power-allocation vector and
broadcasts the index of the codeword. Each sensor will then transmit its analog
observations using its optimal quantized amplification gain. This approach
eliminates the requirement for infinite-rate digital feedback links and is
scalable, especially in large WSNs.Comment: 5 Pages, 3 Figures, 1 Algorithm, Forty Seventh Annual Asilomar
Conference on Signals, Systems, and Computers (ASILOMAR 2013
Continuous-variable entanglement distillation over a pure loss channel with multiple quantum scissors
Entanglement distillation is a key primitive for distributing high-quality
entanglement between remote locations. Probabilistic noiseless linear
amplification based on the quantum scissors is a candidate for entanglement
distillation from noisy continuous-variable (CV) entangled states. Being a
non-Gaussian operation, quantum scissors is challenging to analyze. We present
a derivation of the non-Gaussian state heralded by multiple quantum scissors in
a pure loss channel with two-mode squeezed vacuum input. We choose the reverse
coherent information (RCI)---a proven lower bound on the distillable
entanglement of a quantum state under one-way local operations and classical
communication (LOCC), as our figure of merit. We evaluate a Gaussian lower
bound on the RCI of the heralded state. We show that it can exceed the
unlimited two-way LOCCassisted direct transmission entanglement distillation
capacity of the pure loss channel. The optimal heralded Gaussian RCI with two
quantum scissors is found to be significantly more than that with a single
quantum scissors, albeit at the cost of decreased success probability. Our
results fortify the possibility of a quantum repeater scheme for CV quantum
states using the quantum scissors.Comment: accepted for publication in Physical Review
Linear models of activation cascades: analytical solutions and coarse-graining of delayed signal transduction
Cellular signal transduction usually involves activation cascades, the
sequential activation of a series of proteins following the reception of an
input signal. Here we study the classic model of weakly activated cascades and
obtain analytical solutions for a variety of inputs. We show that in the
special but important case of optimal-gain cascades (i.e., when the
deactivation rates are identical) the downstream output of the cascade can be
represented exactly as a lumped nonlinear module containing an incomplete gamma
function with real parameters that depend on the rates and length of the
cascade, as well as parameters of the input signal. The expressions obtained
can be applied to the non-identical case when the deactivation rates are random
to capture the variability in the cascade outputs. We also show that cascades
can be rearranged so that blocks with similar rates can be lumped and
represented through our nonlinear modules. Our results can be used both to
represent cascades in computational models of differential equations and to fit
data efficiently, by reducing the number of equations and parameters involved.
In particular, the length of the cascade appears as a real-valued parameter and
can thus be fitted in the same manner as Hill coefficients. Finally, we show
how the obtained nonlinear modules can be used instead of delay differential
equations to model delays in signal transduction.Comment: 18 pages, 7 figure
Simple proof of the robustness of Gaussian entanglement in bosonic noisy channels
The extremality of Gaussian states is exploited to show that Gaussian states
are the most robust, among all possible bipartite continuous-variable states at
fixed energy, against disentanglement due to noisy evolutions in Markovian
Gaussian channels involving dissipation and thermal hopping. This proves a
conjecture raised recently in [M. Allegra, P. Giorda, and M. G. A. Paris, Phys.
Rev. Lett. {\bf 105}, 100503 (2010)], providing a rigorous validation of the
conclusions of that work. The problem of identifying continuous variable states
with maximum resilience to entanglement damping in more general bosonic open
system dynamical evolutions, possibly including correlated noise and
non-Markovian effects, remains open.Comment: 3 pages, 1 figure, brief repor
Self-tuning to the Hopf bifurcation in fluctuating systems
The problem of self-tuning a system to the Hopf bifurcation in the presence
of noise and periodic external forcing is discussed. We find that the response
of the system has a non-monotonic dependence on the noise-strength, and
displays an amplified response which is more pronounced for weaker signals. The
observed effect is to be distinguished from stochastic resonance. For the
feedback we have studied, the unforced self-tuned Hopf oscillator in the
presence of fluctuations exhibits sharp peaks in its spectrum. The implications
of our general results are briefly discussed in the context of sound detection
by the inner ear.Comment: 37 pages, 7 figures (8 figure files
On the Effect of Correlated Measurements on the Performance of Distributed Estimation
We address the distributed estimation of an unknown scalar parameter in
Wireless Sensor Networks (WSNs). Sensor nodes transmit their noisy observations
over multiple access channel to a Fusion Center (FC) that reconstructs the
source parameter. The received signal is corrupted by noise and channel fading,
so that the FC objective is to minimize the Mean-Square Error (MSE) of the
estimate. In this paper, we assume sensor node observations to be correlated
with the source signal and correlated with each other as well. The correlation
coefficient between two observations is exponentially decaying with the
distance separation. The effect of the distance-based correlation on the
estimation quality is demonstrated and compared with the case of unity
correlated observations. Moreover, a closed-form expression for the outage
probability is derived and its dependency on the correlation coefficients is
investigated. Numerical simulations are provided to verify our analytic
results.Comment: 5 page
Security proof of quantum key distribution with detection efficiency mismatch
In theory, quantum key distribution (QKD) offers unconditional security based
on the laws of physics. However, as demonstrated in recent quantum hacking
theory and experimental papers, detection efficiency loophole can be fatal to
the security of practical QKD systems. Here, we describe the physical origin of
detection efficiency mismatch in various domains including spatial, spectral,
and time domains and in various experimental set-ups. More importantly, we
prove the unconditional security of QKD even with detection efficiency
mismatch. We explicitly show how the key generation rate is characterized by
the maximal detection efficiency ratio between the two detectors. Furthermore,
we prove that by randomly switching the bit assignments of the detectors, the
effect of detection efficiency mismatch can be completely eliminated.Comment: 35 pages, 7 figure
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