Merging Belief Propagation and the Mean Field Approximation: A Free Energy Approach

Riegler, Erwin; Kirkelund, Gunvor Elisabeth; Manchón, Carles Navarro; Badiu, Mihai-Alin; Fleury, Bernard Henri

doi:10.1109/tit.2012.2218573

Cited by 92 publications

(120 citation statements)

References 34 publications

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“…Compared with BP updating, MF has a simple updating rule, in particular for conjugate-exponential models. However, for multiuser system, the interference cancellation structure in (25) only involves the mean values of interferences while the covariances are not being considered. This makes BP-MF perform poor in the estimation of LLRs for data symbols.…”

Section: B Joint Channel Estimation and Data Decodingmentioning

confidence: 99%

Message-Passing Receiver Design for Joint Channel Estimation and Data Decoding in Uplink Grant-Free SCMA Systems

Wei

Chen

et al. 2019

IEEE Trans. Wireless Commun.

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The conventional grant-based network relies on the handshaking between base station and active users to achieve dynamic multi-user scheduling, which may cost large signaling overheads as well as system latency. To address those problems, the grant-free receiver design is considered in this paper based on sparse code multiple access (SCMA), one of the promising air interface technologies for 5G wireless networks. With the presence of unknown multipath fading, the proposed receiver blindly performs joint channel estimation and data decoding without knowing the user activity in the network. Based on the framework of belief propagation (BP), we formulate a messagepassing receiver for uplink SCMA that performs joint estimation iteratively. However, the direct application of BP for the multivariable detection problem is complex. Motivated by the idea of approximate inference, we use expectation propagation to project the intractable distributions into Gaussian families such that a linear complexity decoder is obtained. Simulation results show that the proposed receiver can detect active users in the network with a high accuracy and can achieve an improved bit-error-rate performance compared with existing methods.

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Section: B Joint Channel Estimation and Data Decodingmentioning

confidence: 99%

Message-Passing Receiver Design for Joint Channel Estimation and Data Decoding in Uplink Grant-Free SCMA Systems

Wei

Chen

et al. 2019

IEEE Trans. Wireless Commun.

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“…Following the definitions in [28,27], a region R of a factor graph consists of subsets of indices I R ⊂ I and F R ⊂ F with the restriction that a ∈ F R implies that S(a) ⊆ I R . Each region R associates a counting number c R ∈ Z.…”

Section: Combined Bp-mf Approximation For Mmttmentioning

confidence: 99%

“…In this case, one simply replaces the filtering in Eq. (27) with smoothing, and the Kalman smoother (for linear models) or nonlinear smoother such as Unscented Rauch-Tung-Striebel Smoother (URTS) [2] can be used.…”

Section: Derivation Of Belief B X (X)mentioning

confidence: 99%

“…Both MF and BP can be implemented in an iterative way, such as message passing (MP). As stated in [27], MF has the virtues of convergent implementation and simple MP update rules for conjugate-exponential models. However, MF is not compatible with hard constraints [28].…”

Section: Introductionmentioning

confidence: 99%

“…However, it may have high complexity. Riegler et al combined BP and MF approximation in a joint MP approach [27].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A message passing approach for multiple maneuvering target tracking

Lan

Wang

et al. 2020

Signal Processing

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This paper considers the problem of detecting and tracking multiple maneuvering targets, which suffers from the intractable inference of high-dimensional latent variables that include target kinematic state, target visibility state, motion mode-model association, and data association. A unified message passing algorithm that combines belief propagation (BP) and mean-field (MF) approximation is proposed for simplifying the intractable inference. By assuming conjugate-exponential priors for target kinematic state, target visibility state, and motion mode-model association, the MF approximation decouples the joint inference of target kinematic state, target visibility state, motion mode-model association into individual low-dimensional inference, yielding simple message passing update equations. The BP is exploited to approximate the probabilities of data association events since it is compatible with hard constraints. Finally, the approximate posterior probability distributions are updated iteratively in a closed-loop manner, which is effective for dealing with the coupling issue between the estimations of target kinematic state and target visibility state and decisions on motion mode-model association and data association. The performance of the proposed algorithm is demonstrated by comparing with the well-known multiple maneuvering target tracking algorithms, including interacting multiple model joint probabilistic data association, interacting multiple model hypothesis-oriented multiple hypothesis tracker and multiple model generalized labeled multi-Bernoulli.

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OTFS and Delay-Doppler Domain Modulation: Signal Detection and Channel Estimation

Guo,

Yuan,

Liu

et al. 2023

Signals and Communication Technology

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Merging Belief Propagation and the Mean Field Approximation: A Free Energy Approach

Cited by 92 publications

References 34 publications

Message-Passing Receiver Design for Joint Channel Estimation and Data Decoding in Uplink Grant-Free SCMA Systems

Message-Passing Receiver Design for Joint Channel Estimation and Data Decoding in Uplink Grant-Free SCMA Systems

A message passing approach for multiple maneuvering target tracking

OTFS and Delay-Doppler Domain Modulation: Signal Detection and Channel Estimation

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