24,425 research outputs found
Distributed Stochastic Optimization over Time-Varying Noisy Network
This paper is concerned with distributed stochastic multi-agent optimization
problem over a class of time-varying network with slowly decreasing
communication noise effects. This paper considers the problem in composite
optimization setting which is more general in noisy network optimization. It is
noteworthy that existing methods for noisy network optimization are Euclidean
projection based. We present two related different classes of non-Euclidean
methods and investigate their convergence behavior. One is distributed
stochastic composite mirror descent type method (DSCMD-N) which provides a more
general algorithm framework than former works in this literature. As a
counterpart, we also consider a composite dual averaging type method (DSCDA-N)
for noisy network optimization. Some main error bounds for DSCMD-N and DSCDA-N
are obtained. The trade-off among stepsizes, noise decreasing rates,
convergence rates of algorithm is analyzed in detail. To the best of our
knowledge, this is the first work to analyze and derive convergence rates of
optimization algorithm in noisy network optimization. We show that an optimal
rate of in nonsmooth convex optimization can be obtained for
proposed methods under appropriate communication noise condition. Moveover,
convergence rates in different orders are comprehensively derived in both
expectation convergence and high probability convergence sense.Comment: 27 page
A Low-Cost Robust Distributed Linearly Constrained Beamformer for Wireless Acoustic Sensor Networks with Arbitrary Topology
We propose a new robust distributed linearly constrained beamformer which
utilizes a set of linear equality constraints to reduce the cross power
spectral density matrix to a block-diagonal form. The proposed beamformer has a
convenient objective function for use in arbitrary distributed network
topologies while having identical performance to a centralized implementation.
Moreover, the new optimization problem is robust to relative acoustic transfer
function (RATF) estimation errors and to target activity detection (TAD)
errors. Two variants of the proposed beamformer are presented and evaluated in
the context of multi-microphone speech enhancement in a wireless acoustic
sensor network, and are compared with other state-of-the-art distributed
beamformers in terms of communication costs and robustness to RATF estimation
errors and TAD errors
Robust Location-Aided Beam Alignment in Millimeter Wave Massive MIMO
Location-aided beam alignment has been proposed recently as a potential
approach for fast link establishment in millimeter wave (mmWave) massive MIMO
(mMIMO) communications. However, due to mobility and other imperfections in the
estimation process, the spatial information obtained at the base station (BS)
and the user (UE) is likely to be noisy, degrading beam alignment performance.
In this paper, we introduce a robust beam alignment framework in order to
exhibit resilience with respect to this problem. We first recast beam alignment
as a decentralized coordination problem where BS and UE seek coordination on
the basis of correlated yet individual position information. We formulate the
optimum beam alignment solution as the solution of a Bayesian team decision
problem. We then propose a suite of algorithms to approach optimality with
reduced complexity. The effectiveness of the robust beam alignment procedure,
compared with classical designs, is then verified on simulation settings with
varying location information accuracies.Comment: 24 pages, 7 figures. The short version of this paper has been
accepted to IEEE Globecom 201
Robust Rotation Synchronization via Low-rank and Sparse Matrix Decomposition
This paper deals with the rotation synchronization problem, which arises in
global registration of 3D point-sets and in structure from motion. The problem
is formulated in an unprecedented way as a "low-rank and sparse" matrix
decomposition that handles both outliers and missing data. A minimization
strategy, dubbed R-GoDec, is also proposed and evaluated experimentally against
state-of-the-art algorithms on simulated and real data. The results show that
R-GoDec is the fastest among the robust algorithms.Comment: The material contained in this paper is part of a manuscript
submitted to CVI
DILAND: An Algorithm for Distributed Sensor Localization with Noisy Distance Measurements
In this correspondence, we present an algorithm for distributed sensor
localization with noisy distance measurements (DILAND) that extends and makes
the DLRE more robust. DLRE is a distributed sensor localization algorithm in
introduced in \cite{usman_loctsp:08}. DILAND operates
when (i) the communication among the sensors is noisy; (ii) the communication
links in the network may fail with a non-zero probability; and (iii) the
measurements performed to compute distances among the sensors are corrupted
with noise. The sensors (which do not know their locations) lie in the convex
hull of at least anchors (nodes that know their own locations.) Under
minimal assumptions on the connectivity and triangulation of each sensor in the
network, this correspondence shows that, under the broad random phenomena
described above, DILAND converges almost surely (a.s.) to the exact sensor
locations.Comment: Submitted to the IEEE Transactions on Signal Processing. Initial
submission on May 2009. 12 page
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