The Factor Graph Approach to Model-Based Signal Processing

Loeliger, Hans-Andrea; Dauwels, Justin; Hu, Junli; Korl, S.; Li, Ping; Kschischang, Frank R.

doi:10.1109/jproc.2007.896497

Cited by 459 publications

(463 citation statements)

References 39 publications

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“…A.2 depict "messages", which are probabilistic information about which pairs of events are coincident and which not. The messages are iteratively computed at each node according to the max-product computation rules (Loeliger et al, 2007). Intuitively, the nodes may be viewed as computing elements that iteratively update their opinion about which events match and which do not, based on the opinions ("messages") they receive from neighboring nodes.…”

Section: A5 Stochastic Event Synchronymentioning

confidence: 99%

A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG

Dauwels

Vialatte

Musha³

et al. 2010

NeuroImage

366

384

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It is well known that EEG signals of Alzheimer's disease (AD) patients are generally less synchronous than in age-matched control subjects. However, this effect is not always easily detectable. This is especially the case for patients in the pre-symptomatic phase, commonly referred to as Mild Cognitive Impairment (MCI), during which neuronal degeneration is occurring prior to the clinical symptoms appearance. In this paper, various synchrony measures are studied in the context of AD diagnosis, including the correlation coefficient, mean-square and phase coherence, Granger causality, phase synchrony indices, information-theoretic divergence measures, state space based measures, and the recently proposed stochastic event synchrony measures. Experiments with EEG data show that many of those measures are strongly correlated (or anti-correlated) with the correlation coefficient, and hence, provide little complementary information about EEG synchrony. Measures that are only weakly correlated with the correlation coefficient include the phase synchrony indices, Granger causality measures, and stochastic-event synchrony measures. In addition, those three families of synchrony measures are mutually uncorrelated, and therefore, they each seem to capture a specific kind of interdependence. For the data set at hand, only two synchrony measures are able to convincingly distinguish AD patients from age-matched control patients, i.e., Granger causality (in particular, full-frequency directed transfer function) and stochastic event synchrony. Those two measures are used as features to distinguish MCI patients from age-matched control subjects, yielding a leave-one-out classification rate of 83%. The classification performance may be further improved by adding complementary features from EEG; this approach may eventually lead to a reliable EEG-based diagnostic tool for MCI and AD.

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Section: A5 Stochastic Event Synchronymentioning

confidence: 99%

A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG

Dauwels

Vialatte

Musha³

et al. 2010

NeuroImage

366

384

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“…For further information, the reader may refer to [30] and the excellent tutorials [32,33]. The reader experienced in factor graphs may skip this section.…”

Section: Factor Graph Reviewmentioning

confidence: 99%

Turbo base stations

Aktas

Hanly

et al. 2011

Cooperative Cellular Wireless Networks

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IntroductionCellular communication systems provide wireless coverage to mobile users across potentially large geographical areas, where base stations (BSs) provide service to users as interfaces to the public telephone network. Cellular communication is based on the principle of dividing a large geographical area into cells which are serviced by separate BSs. Rather than covering a large area by using a single, high-powered BS, cellular systems employ many lower-powered BSs each of which covers a small area. This allows for the reuse of the frequency bands in cells which are not too close to each other, increasing mobile user capacity with a limited spectrum allocation.Traditional narrowband cellular systems require the cochannel interference level to be low. Careful design of frequency reuse among cells is then crucial to maintain cochannel interference at the required low level. The price of low interference, however, is a low frequency reuse factor: only a small portion of the system frequency band can be used in each cell. More recent wideband approaches allow full frequency reuse in each cell, but the cost of that is increased intercell interference. In both approaches, the capacity of a cell in a cellular network, with six surrounding cells, is much less than that of a single cell operating in an intercell interference-free environment. In this chapter, we survey an approach that allows the cell with neighbors to achieve essentially the same capacity as the interference-free cell.In a conventional cellular system, each mobile user is serviced by a single BS, except for the soft-handoff case -a temporary mode of operation where the mobile is moving between cells and is serviced by two base stations. A contrasting idea is to require each mobile station to be serviced by all BSs that are within its reception range. In this approach all the BSs in the cellular network are components of a single transceiver with distributed antennas, an approach known as "network multiple-input multiple-output (MIMO)."Cooperative Cellular Wireless Networks, eds.

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“…However, in those works, only a unidirectional recursion is involved in tracking the time-varying channels, and some of them have relatively high complexity. In this paper, we present a bidirectional channel estimation approach, where the recently proposed Gaussian message passing (GMP) technique [15], [16] is used to exploit the channel correlation information, and the intermediate channel estimation results in the iterative process are employed to perform inter-tap interference cancellation. Compared with the unidirectional approaches, the proposed bidirectional estimation approach can exploit the correlation information of time-varying channels more efſciently, which is essential to improve the system performance, as will be demonstrated by the simulation results.…”

Section: A Low-complexity Iterative Channel Estimation and Detection mentioning

confidence: 99%

“…The parameter β can be determined as in [17]. Equation (34) can be represented by a Forney-style factor graph [15], [16] shown in Fig. 3.…”

Section: A System Modelmentioning

confidence: 99%

See 1 more Smart Citation

A Low-Complexity Iterative Channel Estimation and Detection Technique for Doubly Selective Channels

Guo

Ping

2008

IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference

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In this paper, we propose a low-complexity iterative joint channel estimation, detection and decoding technique for doubly selective channels. The key to the proposed technique is a segment-by-segment processing strategy under the assumption that the channel is approximately static within a short segment of a data block. Through a virtual zero-padding technique, the proposed segment-by-segment equalization approach inherits the low-complexity advantage of the conventional frequency domain equalization (FDE), but does not need the assistance of guard interval (for cyclic-prefixing or zero-padding), thereby avoiding the spectral and power overheads. Furthermore, we develop a low-complexity bidirectional channel estimator, where the Gaussian message passing (GMP) Abstract-In this paper, we propose a low-complexity iterative joint channel estimation, detection and decoding technique for doubly selective channels. The key to the proposed technique is a segment-by-segment processing strategy under the assumption that the channel is approximately static within a short segment of a data block. Through a virtual zero-padding technique, the proposed segment-by-segment equalization approach inherits the low-complexity advantage of the conventional frequency domain equalization (FDE), but does not need the assistance of guard interval (for cyclic-preſxing or zero-padding), thereby avoiding the spectral and power overheads. Furthermore, we develop a lowcomplexity bidirectional channel estimator, where the Gaussian message passing (GMP) technique is used to exploit the channel correlation information, and the intermediate channel estimation results in the iterative process are employed to perform intertap interference cancellation. Simulation results demonstrate the effectiveness of the proposed detection and channel estimation algorithms.

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The Factor Graph Approach to Model-Based Signal Processing

Cited by 459 publications

References 39 publications

A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG

A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG

Turbo base stations

A Low-Complexity Iterative Channel Estimation and Detection Technique for Doubly Selective Channels

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