757 research outputs found
On Cooperative Beamforming Based on Second-Order Statistics of Channel State Information
Cooperative beamforming in relay networks is considered, in which a source
transmits to its destination with the help of a set of cooperating nodes. The
source first transmits locally. The cooperating nodes that receive the source
signal retransmit a weighted version of it in an amplify-and-forward (AF)
fashion. Assuming knowledge of the second-order statistics of the channel state
information, beamforming weights are determined so that the signal-to-noise
ratio (SNR) at the destination is maximized subject to two different power
constraints, i.e., a total (source and relay) power constraint, and individual
relay power constraints. For the former constraint, the original problem is
transformed into a problem of one variable, which can be solved via Newton's
method. For the latter constraint, the original problem is transformed into a
homogeneous quadratically constrained quadratic programming (QCQP) problem. In
this case, it is shown that when the number of relays does not exceed three the
global solution can always be constructed via semidefinite programming (SDP)
relaxation and the matrix rank-one decomposition technique. For the cases in
which the SDP relaxation does not generate a rank one solution, two methods are
proposed to solve the problem: the first one is based on the coordinate descent
method, and the second one transforms the QCQP problem into an infinity norm
maximization problem in which a smooth finite norm approximation can lead to
the solution using the augmented Lagrangian method.Comment: 30 pages, 9 figure
Measurement Matrix Design for Compressive Sensing Based MIMO Radar
In colocated multiple-input multiple-output (MIMO) radar using compressive
sensing (CS), a receive node compresses its received signal via a linear
transformation, referred to as measurement matrix. The samples are subsequently
forwarded to a fusion center, where an L1-optimization problem is formulated
and solved for target information. CS-based MIMO radar exploits the target
sparsity in the angle-Doppler-range space and thus achieves the high
localization performance of traditional MIMO radar but with many fewer
measurements. The measurement matrix is vital for CS recovery performance. This
paper considers the design of measurement matrices that achieve an optimality
criterion that depends on the coherence of the sensing matrix (CSM) and/or
signal-to-interference ratio (SIR). The first approach minimizes a performance
penalty that is a linear combination of CSM and the inverse SIR. The second one
imposes a structure on the measurement matrix and determines the parameters
involved so that the SIR is enhanced. Depending on the transmit waveforms, the
second approach can significantly improve SIR, while maintaining CSM comparable
to that of the Gaussian random measurement matrix (GRMM). Simulations indicate
that the proposed measurement matrices can improve detection accuracy as
compared to a GRMM
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
Blind Estimation of Multiple Carrier Frequency Offsets
Multiple carrier-frequency offsets (CFO) arise in a distributed antenna
system, where data are transmitted simultaneously from multiple antennas. In
such systems the received signal contains multiple CFOs due to mismatch between
the local oscillators of transmitters and receiver. This results in a
time-varying rotation of the data constellation, which needs to be compensated
for at the receiver before symbol recovery. This paper proposes a new approach
for blind CFO estimation and symbol recovery. The received base-band signal is
over-sampled, and its polyphase components are used to formulate a virtual
Multiple-Input Multiple-Output (MIMO) problem. By applying blind MIMO system
estimation techniques, the system response is estimated and used to
subsequently transform the multiple CFOs estimation problem into many
independent single CFO estimation problems. Furthermore, an initial estimate of
the CFO is obtained from the phase of the MIMO system response. The Cramer-Rao
Lower bound is also derived, and the large sample performance of the proposed
estimator is compared to the bound.Comment: To appear in the Proceedings of the 18th Annual IEEE International
Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC),
Athens, Greece, September 3-7, 200
Modified Partition Functions, Consistent Anomalies and Consistent Schwinger Terms
A gauge invariant partition function is defined for gauge theories which
leads to the standard quantization. It is shown that the descent equations and
consequently the consistent anomalies and Schwinger terms can be extracted from
this modified partition function naturally.Comment: 25 page
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