133 research outputs found
On Ergodic Secrecy Capacity for Gaussian MISO Wiretap Channels
A Gaussian multiple-input single-output (MISO) wiretap channel model is
considered, where there exists a transmitter equipped with multiple antennas, a
legitimate receiver and an eavesdropper each equipped with a single antenna. We
study the problem of finding the optimal input covariance that achieves ergodic
secrecy capacity subject to a power constraint where only statistical
information about the eavesdropper channel is available at the transmitter.
This is a non-convex optimization problem that is in general difficult to
solve. Existing results address the case in which the eavesdropper or/and
legitimate channels have independent and identically distributed Gaussian
entries with zero-mean and unit-variance, i.e., the channels have trivial
covariances. This paper addresses the general case where eavesdropper and
legitimate channels have nontrivial covariances. A set of equations describing
the optimal input covariance matrix are proposed along with an algorithm to
obtain the solution. Based on this framework, we show that when full
information on the legitimate channel is available to the transmitter, the
optimal input covariance has always rank one. We also show that when only
statistical information on the legitimate channel is available to the
transmitter, the legitimate channel has some general non-trivial covariance,
and the eavesdropper channel has trivial covariance, the optimal input
covariance has the same eigenvectors as the legitimate channel covariance.
Numerical results are presented to illustrate the algorithm.Comment: 27 pages, 10 figure
On the Coherence Properties of Random Euclidean Distance Matrices
In the present paper we focus on the coherence properties of general random
Euclidean distance matrices, which are very closely related to the respective
matrix completion problem. This problem is of great interest in several
applications such as node localization in sensor networks with limited
connectivity. Our results can directly provide the sufficient conditions under
which an EDM can be successfully recovered with high probability from a limited
number of measurements.Comment: 5 pages, SPAWC 201
Matrix Completion in Colocated MIMO Radar: Recoverability, Bounds & Theoretical Guarantees
It was recently shown that low rank matrix completion theory can be employed
for designing new sampling schemes in the context of MIMO radars, which can
lead to the reduction of the high volume of data typically required for
accurate target detection and estimation. Employing random samplers at each
reception antenna, a partially observed version of the received data matrix is
formulated at the fusion center, which, under certain conditions, can be
recovered using convex optimization. This paper presents the theoretical
analysis regarding the performance of matrix completion in colocated MIMO radar
systems, exploiting the particular structure of the data matrix. Both Uniform
Linear Arrays (ULAs) and arbitrary 2-dimensional arrays are considered for
transmission and reception. Especially for the ULA case, under some mild
assumptions on the directions of arrival of the targets, it is explicitly shown
that the coherence of the data matrix is both asymptotically and approximately
optimal with respect to the number of antennas of the arrays involved and
further, the data matrix is recoverable using a subset of its entries with
minimal cardinality. Sufficient conditions guaranteeing low matrix coherence
and consequently satisfactory matrix completion performance are also presented,
including the arbitrary 2-dimensional array case.Comment: 19 pages, 7 figures, under review in Transactions on Signal
Processing (2013
Compressive Sensing for MIMO Radar
Multiple-input multiple-output (MIMO) radar systems have been shown to
achieve superior resolution as compared to traditional radar systems with the
same number of transmit and receive antennas. This paper considers a
distributed MIMO radar scenario, in which each transmit element is a node in a
wireless network, and investigates the use of compressive sampling for
direction-of-arrival (DOA) estimation. According to the theory of compressive
sampling, a signal that is sparse in some domain can be recovered based on far
fewer samples than required by the Nyquist sampling theorem. The DOA of targets
form a sparse vector in the angle space, and therefore, compressive sampling
can be applied for DOA estimation. The proposed approach achieves the superior
resolution of MIMO radar with far fewer samples than other approaches. This is
particularly useful in a distributed scenario, in which the results at each
receive node need to be transmitted to a fusion center for further processing
Cooperative Beamforming for Wireless Ad Hoc Networks
Via collaborative beamforming, nodes in a wireless network are able to
transmit a common message over long distances in an energy efficient fashion.
However, the process of making available the same message to all collaborating
nodes introduces delays. In this paper, a MAC-PHY cross-layer scheme is
proposed that enables collaborative beamforming at significantly reduced
collaboration overhead. It consists of two phases. In the first phase, nodes
transmit locally in a random access time-slotted fashion. Simultaneous
transmissions from multiple source nodes are viewed as linear mixtures of all
transmitted packets. In the second phase, a set of collaborating nodes, acting
as a distributed antenna system, beamform the received analog waveform to one
or more faraway destinations. This step requires multiplication of the received
analog waveform by a complex weight, which is independently computed by each
cooperating node, and which allows packets bound to the same destination to add
coherently at the destination node. Assuming that each node has access to
location information, the proposed scheme can achieve high throughput, which in
certain cases exceeds one. An analysis of the symbol error probability
corresponding to the proposed scheme is provided.Comment: 5 pages, 4 figures. To appear in the Proceedings of the IEEE Global
Communications Conference (GLOBECOM), Washington, DC, November 26 - 30, 200
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