56 research outputs found
Almost Lossless Analog Compression without Phase Information
We propose an information-theoretic framework for phase retrieval.
Specifically, we consider the problem of recovering an unknown n-dimensional
vector x up to an overall sign factor from m=Rn phaseless measurements with
compression rate R and derive a general achievability bound for R.
Surprisingly, it turns out that this bound on the compression rate is the same
as the one for almost lossless analog compression obtained by Wu and Verd\'u
(2010): Phaseless linear measurements are as good as linear measurements with
full phase information in the sense that ignoring the sign of m measurements
only leaves us with an ambiguity with respect to an overall sign factor of x
Almost Lossless Analog Signal Separation
We propose an information-theoretic framework for analog signal separation.
Specifically, we consider the problem of recovering two analog signals from a
noiseless sum of linear measurements of the signals. Our framework is inspired
by the groundbreaking work of Wu and Verd\'u (2010) on almost lossless analog
compression. The main results of the present paper are a general achievability
bound for the compression rate in the analog signal separation problem, an
exact expression for the optimal compression rate in the case of signals that
have mixed discrete-continuous distributions, and a new technique for showing
that the intersection of generic subspaces with subsets of sufficiently small
Minkowski dimension is empty. This technique can also be applied to obtain a
simplified proof of a key result in Wu and Verd\'u (2010).Comment: To be presented at IEEE Int. Symp. Inf. Theory 2013, Istanbul, Turke
Simultaneous Distributed Sensor Self-Localization and Target Tracking Using Belief Propagation and Likelihood Consensus
We introduce the framework of cooperative simultaneous localization and
tracking (CoSLAT), which provides a consistent combination of cooperative
self-localization (CSL) and distributed target tracking (DTT) in sensor
networks without a fusion center. CoSLAT extends simultaneous localization and
tracking (SLAT) in that it uses also intersensor measurements. Starting from a
factor graph formulation of the CoSLAT problem, we develop a particle-based,
distributed message passing algorithm for CoSLAT that combines nonparametric
belief propagation with the likelihood consensus scheme. The proposed CoSLAT
algorithm improves on state-of-the-art CSL and DTT algorithms by exchanging
probabilistic information between CSL and DTT. Simulation results demonstrate
substantial improvements in both self-localization and tracking performance.Comment: 10 pages, 5 figure
Generic Correlation Increases Noncoherent MIMO Capacity
We study the high-SNR capacity of MIMO Rayleigh block-fading channels in the
noncoherent setting where neither transmitter nor receiver has a priori channel
state information. We show that when the number of receive antennas is
sufficiently large and the temporal correlation within each block is "generic"
(in the sense used in the interference-alignment literature), the capacity
pre-log is given by T(1-1/N) for T<N, where T denotes the number of transmit
antennas and N denotes the block length. A comparison with the widely used
constant block-fading channel (where the fading is constant within each block)
shows that for a large block length, generic correlation increases the capacity
pre-log by a factor of about four.Comment: To be presented at IEEE Int. Symp. Inf. Theory (ISIT) 2013, Istanbul,
Turke
Oversampling Increases the Pre-Log of Noncoherent Rayleigh Fading Channels
We analyze the capacity of a continuous-time, time-selective, Rayleigh
block-fading channel in the high signal-to-noise ratio (SNR) regime. The fading
process is assumed stationary within each block and to change independently
from block to block; furthermore, its realizations are not known a priori to
the transmitter and the receiver (noncoherent setting). A common approach to
analyzing the capacity of this channel is to assume that the receiver performs
matched filtering followed by sampling at symbol rate (symbol matched
filtering). This yields a discrete-time channel in which each transmitted
symbol corresponds to one output sample. Liang & Veeravalli (2004) showed that
the capacity of this discrete-time channel grows logarithmically with the SNR,
with a capacity pre-log equal to . Here, is the number of
symbols transmitted within one fading block, and is the rank of the
covariance matrix of the discrete-time channel gains within each fading block.
In this paper, we show that symbol matched filtering is not a
capacity-achieving strategy for the underlying continuous-time channel.
Specifically, we analyze the capacity pre-log of the discrete-time channel
obtained by oversampling the continuous-time channel output, i.e., by sampling
it faster than at symbol rate. We prove that by oversampling by a factor two
one gets a capacity pre-log that is at least as large as . Since the
capacity pre-log corresponding to symbol-rate sampling is , our result
implies indeed that symbol matched filtering is not capacity achieving at high
SNR.Comment: To appear in the IEEE Transactions on Information Theor
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