11,301 research outputs found
Information-theoretic analysis of MIMO channel sounding
The large majority of commercially available multiple-input multiple-output
(MIMO) radio channel measurement devices (sounders) is based on time-division
multiplexed switching (TDMS) of a single transmit/receive radio-frequency chain
into the elements of a transmit/receive antenna array. While being
cost-effective, such a solution can cause significant measurement errors due to
phase noise and frequency offset in the local oscillators. In this paper, we
systematically analyze the resulting errors and show that, in practice,
overestimation of channel capacity by several hundred percent can occur.
Overestimation is caused by phase noise (and to a lesser extent frequency
offset) leading to an increase of the MIMO channel rank. Our analysis
furthermore reveals that the impact of phase errors is, in general, most
pronounced if the physical channel has low rank (typical for line-of-sight or
poor scattering scenarios). The extreme case of a rank-1 physical channel is
analyzed in detail. Finally, we present measurement results obtained from a
commercially employed TDMS-based MIMO channel sounder. In the light of the
findings of this paper, the results obtained through MIMO channel measurement
campaigns using TDMS-based channel sounders should be interpreted with great
care.Comment: 99 pages, 14 figures, submitted to IEEE Transactions on Information
Theor
Cutting Plane Algorithms are Exact for Euclidean Max-Sum Problems
This paper studies binary quadratic programs in which the objective is
defined by a Euclidean distance matrix, subject to a general polyhedral
constraint set. This class of nonconcave maximisation problems includes the
capacitated, generalised and bi-level diversity problems as special cases. We
introduce two exact cutting plane algorithms to solve this class of
optimisation problems. The new algorithms remove the need for a concave
reformulation, which is known to significantly slow down convergence. We
establish exactness of the new algorithms by examining the concavity of the
quadratic objective in a given direction, a concept we refer to as directional
concavity. Numerical results show that the algorithms outperform other exact
methods for benchmark diversity problems (capacitated, generalised and
bi-level), and can easily solve problems of up to three thousand variables
A methodology for airplane parameter estimation and confidence interval determination in nonlinear estimation problems
An algorithm for maximum likelihood (ML) estimation is developed with an efficient method for approximating the sensitivities. The ML algorithm relies on a new optimization method referred to as a modified Newton-Raphson with estimated sensitivities (MNRES). MNRES determines sensitivities by using slope information from local surface approximations of each output variable in parameter space. With the fitted surface, sensitivity information can be updated at each iteration with less computational effort than that required by either a finite-difference method or integration of the analytically determined sensitivity equations. MNRES eliminates the need to derive sensitivity equations for each new model, and thus provides flexibility to use model equations in any convenient format. A random search technique for determining the confidence limits of ML parameter estimates is applied to nonlinear estimation problems for airplanes. The confidence intervals obtained by the search are compared with Cramer-Rao (CR) bounds at the same confidence level. The degree of nonlinearity in the estimation problem is an important factor in the relationship between CR bounds and the error bounds determined by the search technique. Beale's measure of nonlinearity is developed in this study for airplane identification problems; it is used to empirically correct confidence levels and to predict the degree of agreement between CR bounds and search estimates
Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
The affine rank minimization problem consists of finding a matrix of minimum
rank that satisfies a given system of linear equality constraints. Such
problems have appeared in the literature of a diverse set of fields including
system identification and control, Euclidean embedding, and collaborative
filtering. Although specific instances can often be solved with specialized
algorithms, the general affine rank minimization problem is NP-hard. In this
paper, we show that if a certain restricted isometry property holds for the
linear transformation defining the constraints, the minimum rank solution can
be recovered by solving a convex optimization problem, namely the minimization
of the nuclear norm over the given affine space. We present several random
ensembles of equations where the restricted isometry property holds with
overwhelming probability. The techniques used in our analysis have strong
parallels in the compressed sensing framework. We discuss how affine rank
minimization generalizes this pre-existing concept and outline a dictionary
relating concepts from cardinality minimization to those of rank minimization
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