Gaussian belief propagation based multiuser detection

Bickson, Danny; Dolev, Danny; Shental, Ori; Siegel, Paul H.; Wolf, J.K.

doi:10.1109/isit.2008.4595314

Cited by 69 publications

(96 citation statements)

References 72 publications

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“…Figure 7 illustrates that the mean square error achieved by NBP is almost indistinguishable from the MMSE bound (16).…”

Section: Information Theoretic Characterizationmentioning

confidence: 93%

“…For example, in multi-user detection for code division multiple access (CDMA) systems [13][14][15][16][17], the system measurements are given by the received noisy wireless signal and the goal is to estimate the transmitted bit pattern, which plays the role of the fault pattern. One important difference is that in multi-user detection, each bit typically has an equal probability of being 0 or 1, whereas in fault identification the prior probability that a bit is 1 (indicating that a fault is present) is typically much lower.…”

Section: B Related Workmentioning

confidence: 99%

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Fault Identification Via Nonparametric Belief Propagation

Bickson

Baron

Ihler

et al. 2011

IEEE Trans. Signal Process.

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Abstract-We consider the problem of identifying a pattern of faults from a set of noisy linear measurements. Unfortunately, maximum a posteriori probability estimation of the fault pattern is computationally intractable. To solve the fault identification problem, we propose a non-parametric belief propagation approach. We show empirically that our belief propagation solver is more accurate than recent state-of-the-art algorithms including interior point methods and semidefinite programming. Our superior performance is explained by the fact that we take into account both the binary nature of the individual faults and the sparsity of the fault pattern arising from their rarity.Index Terms-compressed sensing, fault identification, message passing, non-parametric belief propagation, stochastic approximation.

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“…Figure 7 illustrates that the mean square error achieved by NBP is almost indistinguishable from the MMSE bound (16).…”

Section: Information Theoretic Characterizationmentioning

confidence: 93%

Section: B Related Workmentioning

confidence: 99%

Fault Identification Via Nonparametric Belief Propagation

Bickson

Baron

Ihler

et al. 2011

IEEE Trans. Signal Process.

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“…Another way to get the relaxation of quadratic problem is to use semidefinite programming [13], [3], which results in polynomial-time solvable convex optimization problem. Such problems can be also solved in decentralized environment (within a bounded accuracy), see for example algorithms based on Gaussian belief propagation [2]. Both approaches increase the number of decision variables and the number of constraints.…”

Section: Decentralized Rounding Algorithmmentioning

confidence: 99%

Decentralized Approximation Algorithm for Data Placement Problem in Content Delivery Networks

Drwal

Józefczyk

2012

Technological Innovation for Value Creation

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Abstract. Recent advancements in Internet technology research, as well as the widespread of commercial content delivery networks, motivates the need for optimization algorithms designed to work in decentralized manner. In this paper we formulate data placement problem, a special case of universal facility location problem with quadratic terms in objective function. The considered combinatorial optimization problem is NP-hard. A randomized algorithm is presented that approximates the solution within factor O(log n) in decentralized environment, assuming asynchronous message passing of bounded sizes.

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“…The Kalman filter is an efficient iterative algorithm to estimate the state of a discrete-time controlled process x ∈ R n that is governed by the linear stochastic difference equation 1 :…”

Section: A An Overviewmentioning

confidence: 99%

“…Recent work [1] shows how to compute efficiently and distributively the MMSE prediction for the multiuser detection problem, using the Gaussian Belief Propagation (GaBP) algorithm. The basic idea is to shift the problem from linear algebra domain into a probabilistic graphical model, solving an equivalent inference problem using the efficient belief propagation inference engine.…”

Section: Introductionmentioning

confidence: 99%

Distributed Kalman filter via Gaussian Belief Propagation

Bickson

Shental²,

Dolev

2008

2008 46th Annual Allerton Conference on Communication, Control, and Computing

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Abstract-Recent result shows how to compute distributively and efficiently the linear MMSE for the multiuser detection problem, using the Gaussian BP algorithm. In the current work, we extend this construction, and show that operating this algorithm twice on the matching inputs, has several interesting interpretations. First, we show equivalence to computing one iteration of the Kalman filter. Second, we show that the Kalman filter is a special case of the Gaussian information bottleneck algorithm, when the weight parameter β = 1. Third, we discuss the relation to the Affine-scaling interior-point method and show it is a special case of Kalman filter.Besides of the theoretical interest of this linking estimation, compression/clustering and optimization, we allow a single distributed implementation of those algorithms, which is a highly practical and important task in sensor and mobile ad-hoc networks. Application to numerous problem domains includes collaborative signal processing and distributed allocation of resources in a communication network.

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Gaussian belief propagation based multiuser detection

Cited by 69 publications

References 72 publications

Fault Identification Via Nonparametric Belief Propagation

Fault Identification Via Nonparametric Belief Propagation

Decentralized Approximation Algorithm for Data Placement Problem in Content Delivery Networks

Distributed Kalman filter via Gaussian Belief Propagation

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