33,832 research outputs found
Reconsidering Linear Transmit Signal Processing in 1-Bit Quantized Multi-User MISO Systems
In this contribution, we investigate a coarsely quantized Multi-User
(MU)-Multiple Input Single Output (MISO) downlink communication system, where
we assume 1-Bit Digital-to-Analog Converters (DACs) at the Base Station (BS)
antennas. First, we analyze the achievable sum rate lower-bound using the
Bussgang decomposition. In the presence of the non-linear quanization, our
analysis indicates the potential merit of reconsidering traditional signal
processing techniques in coarsely quantized systems, i.e., reconsidering
transmit covariance matrices whose rank is equal to the rank of the channel.
Furthermore, in the second part of this paper, we propose a linear precoder
design which achieves the predicted increase in performance compared with a
state of the art linear precoder design. Moreover, our linear signal processing
algorithm allows for higher-order modulation schemes to be employed
Quantifying entanglement of formation for two-mode Gaussian states: Analytical expressions for upper and lower bounds and numerical estimation of its exact value
Entanglement of formation quantifies the entanglement of a state in terms of
the entropy of entanglement of the least entangled pure state needed to prepare
it. An analytical expression for this measure exists only for special cases,
and finding a closed formula for an arbitrary state still remains an open
problem. In this work we focus on two-mode Gaussian states, and we derive
narrow upper and lower bounds for the measure that get tight for several
special cases. Further, we show that the problem of calculating the actual
value of the entanglement of formation for arbitrary two-mode Gaussian states
reduces to a trivial single parameter optimization process, and we provide an
efficient algorithm for the numerical calculation of the measure.Comment: 5 pages, 2 figures In this third version a few typos of the first and
second versions have been correcte
Data Transmission Over Networks for Estimation and Control
We consider the problem of controlling a linear time invariant process when the controller is located at a location remote from where the sensor measurements are being generated. The communication from the sensor to the controller is supported by a communication network with arbitrary topology composed of analog erasure channels. Using a separation principle, we prove that the optimal linear-quadratic-Gaussian (LQG) controller consists of an LQ optimal regulator along with an estimator that estimates the state of the process across the communication network. We then determine the optimal information processing strategy that should be followed by each node in the network so that the estimator is able to compute the best possible estimate in the minimum mean squared error sense. The algorithm is optimal for any packet-dropping process and at every time step, even though it is recursive and hence requires a constant amount of memory, processing and transmission at every node in the network per time step. For the case when the packet drop processes are memoryless and independent across links, we analyze the stability properties and the performance of the closed loop system. The algorithm is an attempt to escape the viewpoint of treating a network of communication links as a single end-to-end link with the probability of successful transmission determined by some measure of the reliability of the network
Noisy independent component analysis of auto-correlated components
We present a new method for the separation of superimposed, independent,
auto-correlated components from noisy multi-channel measurement. The presented
method simultaneously reconstructs and separates the components, taking all
channels into account and thereby increases the effective signal-to-noise ratio
considerably, allowing separations even in the high noise regime.
Characteristics of the measurement instruments can be included, allowing for
application in complex measurement situations. Independent posterior samples
can be provided, permitting error estimates on all desired quantities. Using
the concept of information field theory, the algorithm is not restricted to any
dimensionality of the underlying space or discretization scheme thereof
Fast calculation of the Fisher matrix for cosmic microwave background experiments
The Fisher information matrix of the cosmic microwave background (CMB)
radiation power spectrum coefficients is a fundamental quantity that specifies
the information content of a CMB experiment. In the most general case, its
exact calculation scales with the third power of the number of data points N
and is therefore computationally prohibitive for state-of-the-art surveys.
Applicable to a very large class of CMB experiments without special symmetries,
we show how to compute the Fisher matrix in only O(N^2 log N) operations as
long as the inverse noise covariance matrix can be applied to a data vector in
time O(l_max^3 log l_max). This assumption is true to a good approximation for
all CMB data sets taken so far. The method takes into account common
systematics such as arbitrary sky coverage and realistic noise correlations. As
a consequence, optimal quadratic power spectrum estimation also becomes
feasible in O(N^2 log N) operations for this large group of experiments. We
discuss the relevance of our findings to other areas of cosmology where optimal
power spectrum estimation plays a role.Comment: 4 pages, 1 figures. Accepted for publication in Astronomy and
Astrophysics Letters. Replaced to match published versio
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