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Decentralized sequential change detection using physical layer fusion
 in Proc. 2007 IEEE Int. Symp. on Inform. Theory
, 2007
"... The problem of decentralized sequential detection with conditionally independent observations is studied. The sensors form a star topology with a central node called fusion center as the hub. The sensors transmit a simple function of their observations in an analog fashion over a wireless Gaussian m ..."
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Cited by 13 (3 self)
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The problem of decentralized sequential detection with conditionally independent observations is studied. The sensors form a star topology with a central node called fusion center as the hub. The sensors transmit a simple function of their observations in an analog fashion over a wireless Gaussian multiple access channel and operate under either a power constraint or an energy constraint. The optimal transmission strategy at each stage is shown to be the one that maximizes a certain AliSilvey distance between the distributions for the hypotheses before and after the change. Simulations demonstrate that the proposed analog technique has lower detection delays when compared with existing schemes. Simulations further demonstrate that the energyconstrained formulation enables better use of the total available energy than the powerconstrained formulation in the change detection problem.
1 Decentralized Sequential Change Detection Using Physical Layer Fusion
, 707
"... Abstract — The problem of decentralized sequential detection with conditionally independent observations is studied. The sensors form a star topology with a central node called fusion center as the hub. The sensors make noisy observations of a parameter that changes from an initial state to a final ..."
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Abstract — The problem of decentralized sequential detection with conditionally independent observations is studied. The sensors form a star topology with a central node called fusion center as the hub. The sensors make noisy observations of a parameter that changes from an initial state to a final state at a random time where the random change time has a geometric distribution. The sensors amplify and forward the observations over a wireless Gaussian multiple access channel and operate under either a power constraint or an energy constraint. The optimal transmission strategy at each stage is shown to be the one that maximizes a certain AliSilvey distance between the distributions for the hypotheses before and after the change. Simulations demonstrate that the proposed analog technique has lower detection delays when compared with existing schemes. Simulations further demonstrate that the energyconstrained formulation enables better use of the total available energy than the powerconstrained formulation in the change detection problem. Index Terms — AliSilvey distance, change detection, correlation, Markov decision process, multiple access channel, sequential
An Analog MVUE for a Wireless Sensor Network
"... Abstract — An analog minimumvariance unbiased estimator (MVUE) over an asymmetric wireless sensor network is studied. Minimisation of variance is cast into a constrained nonconvex optimisation problem. An explicit algorithm that solves the problem is provided. The solution is obtained by decomposi ..."
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Abstract — An analog minimumvariance unbiased estimator (MVUE) over an asymmetric wireless sensor network is studied. Minimisation of variance is cast into a constrained nonconvex optimisation problem. An explicit algorithm that solves the problem is provided. The solution is obtained by decomposing the original problem into a finite number of convex optimisation problems with explicit solutions. These solutions are then juxtaposed together by exploiting further structure in the objective function. I. INTRODUCTION AND PROBLEM STATEMENT In this paper, we study a distributed analog minimum variance unbiased estimator (MVUE) over an asymmetric wireless sensor network. In a typical sensor network, sensors communicate their observations with a central node via a
Convex Separable Problems with Linear Constraints in Signal Processing and Communications
, 2014
"... In this work, we focus on separable convex optimization problems with box constraints and a specific set of linear constraints. The solution is given in closedform as a function of some Lagrange multipliers that can be computed through an iterative procedure in a finite number of steps. Graphical i ..."
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In this work, we focus on separable convex optimization problems with box constraints and a specific set of linear constraints. The solution is given in closedform as a function of some Lagrange multipliers that can be computed through an iterative procedure in a finite number of steps. Graphical interpretations are given casting valuable insights into the proposed algorithm and allowing to retain some of the intuition spelled out by the waterfilling policy. It turns out that it is not only general enough to compute the solution to different instances of the problem at hand but also remarkably simple in the way it operates. We also show how some power allocation problems in signal processing and communications can be solved with the proposed algorithm.
A Decomposition Algorithm for Nested Resource Allocation Problems
, 2014
"... Abstract. We propose an exact polynomial algorithm for a resource allocation problem with convex costs and constraints on partial sums of resource consumptions, in the presence of either continuous or integer variables. No assumption of strict convexity or differentiability is needed. The method so ..."
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Abstract. We propose an exact polynomial algorithm for a resource allocation problem with convex costs and constraints on partial sums of resource consumptions, in the presence of either continuous or integer variables. No assumption of strict convexity or differentiability is needed. The method solves a hierarchy of resource allocation subproblems, whose solutions are used to convert constraints on sums of resources into bounds for separate variables at higher levels. The resulting time complexity for the integer problem is O(n logm log(B/n)), and the complexity of obtaining an approximate solution for the continuous case is O(n logm log(B/)), n being the number of variables, m the number of ascending constraints (such that m < n), a desired precision, and B the total resource. This algorithm attains the bestknown complexity when m = n, and improves it when logm = o(log n). Extensive experimental analyses are conducted with four recent algorithms on various continuous problems issued from theory and practice. The proposed method achieves a higher performance than previous algorithms, addressing all problems with up to one million variables in less than one minute on a modern computer.
CONVEX SEPARABLE PROBLEMS WITH LINEAR AND BOX CONSTRAINTS
, 2014
"... In this work, we focus on separable convex optimization problems with linear and box constraints and compute the solution in closedform as a function of some Lagrange multipliers that can be easily computed in a finite number of iterations. This allows us to bridge the gap between a wide family of ..."
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In this work, we focus on separable convex optimization problems with linear and box constraints and compute the solution in closedform as a function of some Lagrange multipliers that can be easily computed in a finite number of iterations. This allows us to bridge the gap between a wide family of power allocation problems of practical interest in signal processing and communications and their efficient implementation in practice.
Power Minimization for CDMA under Colored Noise
"... Abstract—Rateconstrained power minimization (PMIN) over a code division multipleaccess (CDMA) channel with correlated noise is studied. PMIN is shown to be an instance of a separable convex optimization problem subject to linear ascending constraints. PMIN is further reduced to a dual problem of ..."
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Abstract—Rateconstrained power minimization (PMIN) over a code division multipleaccess (CDMA) channel with correlated noise is studied. PMIN is shown to be an instance of a separable convex optimization problem subject to linear ascending constraints. PMIN is further reduced to a dual problem of sumrate maximization (RMAX). The results highlight the underlying unity between PMIN, RMAX, and a problem closely related to PMIN but with linear receiver constraints. Subsequently, conceptually simple sequence design algorithms are proposed to explicitly identify an assignment of sequences and powers that solve PMIN. The algorithms yield an upper bound of 2