Belief propagation (BP) is widely used to solve the cooperative localization problem due its excellent performance and natural distributed structure of implementation. For a mobile agent network, factor graph inevitably encounters loops. In this case, BP algorithm becomes iterative can only provide an approximate marginal probability density function estimate with finite iterations. We propose ...

Belief propagation (BP) algorithm has been becoming increasingly a popular method for probabilstic inference on general graphical models. When networks have loops, it may not converge and, even if converges, beliefs, Le., the result of the algorithm, may not be equal to exact marginal probabilties. When networks have loops, the algorithm is called Loopy BP (LBP). Tatikonda and Jordan applied Gi...

Receiver algorithms which combine belief propagation (BP) with the mean field (MF) approximation are well-suited for inference of both continuous and discrete random variables. In wireless scenarios involving detection of multiple signals, the standard construction of the combined BP-MF framework includes the equalization or multi-user detection functions within the MF subgraph. In this paper, ...

In this work, we consider the problem of reduced latency of low-density parity-check (LDPC) codes with iterative detection and decoding (IDD) receiver in multiuser multipleantenna systems. The proposed knowledge-aided IDD (KA-IDD) system employs a minimum mean-square error detector with refined iterative processing and a reweighted belief propagation (BP) decoding algorithm. We present reweight...

Collective graphical models (CGMs) are a formalism for inference and learning with aggregate data that are motivated by a model for bird migration. We highlight a close connection between approximate MAP inference in CGMs and marginal inference in standard graphical models. The connection leads us to derive a novel Belief Propagation (BP)-style algorithm for collective graphical models. The alg...

In this paper, the minimum span of stopping sets of regular LDPC codes are defined and analyzed. The minimum span of an LDPC code is closely related to immunity to burst erasures when an LDPC code is decoded with belief propagation (BP) for erasure channels. The minimum span of stopping sets is the smallest span of a non-empty stopping set. If a single erasure burst of length shorter than the m...

Low-Density Parity-Check (LDPC) codes have gained lots of popularity due to their capacity achieving/approaching property. This work studies the iterative decoding also known as message-passing algorithms applied to LDPC codes. Belief propagation (BP) algorithm and its approximations, most notably min-sum (MS), are popular iterative decoding algorithms used for LDPC and turbo codes. The thesis ...

EMBP is a variant of Belief Propagation (BP) that always converges, even on graphical models with loops. Its initial ad hoc development was driven by the search for likely variable assignments in Constraint Satisfaction problems, but in general it can estimate marginal probabilities over any model that BP can. Thus we derive a canonical version of EMBP from first principles by applying the Expe...

In a complete bipartite graph with vertex sets of cardinalities n and n′, assign random weights from exponential distribution with mean 1, independently to each edge. We show that, as n → ∞, with n′ = dn/αe for any fixed α > 1, the minimum weight of many-to-one matchings converges to a constant (depending on α). Many-to-one matching arises as an optimization step in an algorithm for genome sequ...

Belief propagation is a message-passing algorithm for performing inference in graphical models that has been implemented in a variety of domains including artificial intelligence, information theory and statistical physics. As we know, BP provides exact results only on a relatively restrictive subclass of problems, when the graphical model is a tree (or more generally if each connected componen...