440 research outputs found
Fault-tolerant control under controller-driven sampling using virtual actuator strategy
We present a new output feedback fault tolerant control strategy for
continuous-time linear systems. The strategy combines a digital nominal
controller under controller-driven (varying) sampling with virtual-actuator
(VA)-based controller reconfiguration to compensate for actuator faults. In the
proposed scheme, the controller controls both the plant and the sampling
period, and performs controller reconfiguration by engaging in the loop the VA
adapted to the diagnosed fault. The VA also operates under controller-driven
sampling. Two independent objectives are considered: (a) closed-loop stability
with setpoint tracking and (b) controller reconfiguration under faults. Our
main contribution is to extend an existing VA-based controller reconfiguration
strategy to systems under controller-driven sampling in such a way that if
objective (a) is possible under controller-driven sampling (without VA) and
objective (b) is possible under uniform sampling (without controller-driven
sampling), then closed-loop stability and setpoint tracking will be preserved
under both healthy and faulty operation for all possible sampling rate
evolutions that may be selected by the controller
Modulation Classification for MIMO-OFDM Signals via Approximate Bayesian Inference
The problem of modulation classification for a multiple-antenna (MIMO) system
employing orthogonal frequency division multiplexing (OFDM) is investigated
under the assumption of unknown frequency-selective fading channels and
signal-to-noise ratio (SNR). The classification problem is formulated as a
Bayesian inference task, and solutions are proposed based on Gibbs sampling and
mean field variational inference. The proposed methods rely on a selection of
the prior distributions that adopts a latent Dirichlet model for the modulation
type and on the Bayesian network formalism. The Gibbs sampling method converges
to the optimal Bayesian solution and, using numerical results, its accuracy is
seen to improve for small sample sizes when switching to the mean field
variational inference technique after a number of iterations. The speed of
convergence is shown to improve via annealing and random restarts. While most
of the literature on modulation classification assume that the channels are
flat fading, that the number of receive antennas is no less than that of
transmit antennas, and that a large number of observed data symbols are
available, the proposed methods perform well under more general conditions.
Finally, the proposed Bayesian methods are demonstrated to improve over
existing non-Bayesian approaches based on independent component analysis and on
prior Bayesian methods based on the `superconstellation' method.Comment: To be appear in IEEE Trans. Veh. Technolog
Opportunistic Relaying in Wireless Networks
Relay networks having source-to-destination pairs and half-duplex
relays, all operating in the same frequency band in the presence of block
fading, are analyzed. This setup has attracted significant attention and
several relaying protocols have been reported in the literature. However, most
of the proposed solutions require either centrally coordinated scheduling or
detailed channel state information (CSI) at the transmitter side. Here, an
opportunistic relaying scheme is proposed, which alleviates these limitations.
The scheme entails a two-hop communication protocol, in which sources
communicate with destinations only through half-duplex relays. The key idea is
to schedule at each hop only a subset of nodes that can benefit from
\emph{multiuser diversity}. To select the source and destination nodes for each
hop, it requires only CSI at receivers (relays for the first hop, and
destination nodes for the second hop) and an integer-value CSI feedback to the
transmitters. For the case when is large and is fixed, it is shown that
the proposed scheme achieves a system throughput of bits/s/Hz. In
contrast, the information-theoretic upper bound of bits/s/Hz
is achievable only with more demanding CSI assumptions and cooperation between
the relays. Furthermore, it is shown that, under the condition that the product
of block duration and system bandwidth scales faster than , the
achievable throughput of the proposed scheme scales as .
Notably, this is proven to be the optimal throughput scaling even if
centralized scheduling is allowed, thus proving the optimality of the proposed
scheme in the scaling law sense.Comment: 17 pages, 8 figures, To appear in IEEE Transactions on Information
Theor
Spatial Compressive Sensing for MIMO Radar
We study compressive sensing in the spatial domain to achieve target
localization, specifically direction of arrival (DOA), using multiple-input
multiple-output (MIMO) radar. A sparse localization framework is proposed for a
MIMO array in which transmit and receive elements are placed at random. This
allows for a dramatic reduction in the number of elements needed, while still
attaining performance comparable to that of a filled (Nyquist) array. By
leveraging properties of structured random matrices, we develop a bound on the
coherence of the resulting measurement matrix, and obtain conditions under
which the measurement matrix satisfies the so-called isotropy property. The
coherence and isotropy concepts are used to establish uniform and non-uniform
recovery guarantees within the proposed spatial compressive sensing framework.
In particular, we show that non-uniform recovery is guaranteed if the product
of the number of transmit and receive elements, MN (which is also the number of
degrees of freedom), scales with K(log(G))^2, where K is the number of targets
and G is proportional to the array aperture and determines the angle
resolution. In contrast with a filled virtual MIMO array where the product MN
scales linearly with G, the logarithmic dependence on G in the proposed
framework supports the high-resolution provided by the virtual array aperture
while using a small number of MIMO radar elements. In the numerical results we
show that, in the proposed framework, compressive sensing recovery algorithms
are capable of better performance than classical methods, such as beamforming
and MUSIC.Comment: To appear in IEEE Transactions on Signal Processin
Target Localization Accuracy Gain in MIMO Radar Based Systems
This paper presents an analysis of target localization accuracy, attainable
by the use of MIMO (Multiple-Input Multiple-Output) radar systems, configured
with multiple transmit and receive sensors, widely distributed over a given
area. The Cramer-Rao lower bound (CRLB) for target localization accuracy is
developed for both coherent and non-coherent processing. Coherent processing
requires a common phase reference for all transmit and receive sensors. The
CRLB is shown to be inversely proportional to the signal effective bandwidth in
the non-coherent case, but is approximately inversely proportional to the
carrier frequency in the coherent case. We further prove that optimization over
the sensors' positions lowers the CRLB by a factor equal to the product of the
number of transmitting and receiving sensors. The best linear unbiased
estimator (BLUE) is derived for the MIMO target localization problem. The
BLUE's utility is in providing a closed form localization estimate that
facilitates the analysis of the relations between sensors locations, target
location, and localization accuracy. Geometric dilution of precision (GDOP)
contours are used to map the relative performance accuracy for a given layout
of radars over a given geographic area.Comment: 36 pages, 5 figures, submitted to IEEE Transaction on Information
Theor
A PTAS for Bounded-Capacity Vehicle Routing in Planar Graphs
The Capacitated Vehicle Routing problem is to find a minimum-cost set of
tours that collectively cover clients in a graph, such that each tour starts
and ends at a specified depot and is subject to a capacity bound on the number
of clients it can serve. In this paper, we present a polynomial-time
approximation scheme (PTAS) for instances in which the input graph is planar
and the capacity is bounded. Previously, only a quasipolynomial-time
approximation scheme was known for these instances. To obtain this result, we
show how to embed planar graphs into bounded-treewidth graphs while preserving,
in expectation, the client-to-client distances up to a small additive error
proportional to client distances to the depot
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