54,869 research outputs found
Differential Amplify-and-Forward Relaying in Time-Varying Rayleigh Fading Channels
This paper considers the performance of differential amplify-and-forward
(D-AF) relaying over time-varying Rayleigh fading channels. Using the
auto-regressive time-series model to characterize the time-varying nature of
the wireless channels, new weights for the maximum ratio combining (MRC) of the
received signals at the destination are proposed. Expression for the pair-wise
error probability (PEP) is provided and used to obtain an approximation of the
total average bit error probability (BEP). The obtained BEP approximation
clearly shows how the system performance depends on the auto-correlation of the
direct and the cascaded channels and an irreducible error floor exists at high
signal-to-noise ratio (SNR). Simulation results also demonstrate that, for
fast-fading channels, the new MRC weights lead to a better performance when
compared to the classical combining scheme. Our analysis is verified with
simulation results in different fading scenarios
Entanglement witnesses arising from Choi type positive linear maps
We construct optimal PPTES witnesses to detect PPT entangled
edge states of type constructed recently \cite{kye_osaka}. To do this,
we consider positive linear maps which are variants of the Choi type map
involving complex numbers, and examine several notions related to optimality
for those entanglement witnesses. Through the discussion, we suggest a method
to check the optimality of entanglement witnesses without the spanning
property.Comment: 18 pages, 4 figures, 1 tabl
Determining White Noise Forcing From Eulerian Observations in the Navier Stokes Equation
The Bayesian approach to inverse problems is of paramount importance in
quantifying uncertainty about the input to and the state of a system of
interest given noisy observations. Herein we consider the forward problem of
the forced 2D Navier Stokes equation. The inverse problem is inference of the
forcing, and possibly the initial condition, given noisy observations of the
velocity field. We place a prior on the forcing which is in the form of a
spatially correlated temporally white Gaussian process, and formulate the
inverse problem for the posterior distribution. Given appropriate spatial
regularity conditions, we show that the solution is a continuous function of
the forcing. Hence, for appropriately chosen spatial regularity in the prior,
the posterior distribution on the forcing is absolutely continuous with respect
to the prior and is hence well-defined. Furthermore, the posterior distribution
is a continuous function of the data. We complement this theoretical result
with numerical simulation of the posterior distribution
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