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The growing number of Internet of Thing (IoT) and Ultra-Reliable Low Latency Communications (URLCC) use cases in next generation communication networks calls for the development of efficient Forward Error Correction (FEC) mechanisms. These use cases usually imply using short to mid-sized information blocks and requires low-complexity and/or fast decoding procedures. This paper investigates the joint learning of short to mid block-length coding schemes and associated Belief-Propagation (BP) like decoders using Machine Learning (ML) techniques. An interpretable auto-encoder (AE) architecture is proposed, ensuring scalability to block sizes currently challenging for ML-based linear block code design approaches. By optimizing a coding scheme w.r.t. the targeted decoder, the proposed system offers a good complexity/performance trade-off compared to various codes from literature with length up to 128 bits. [ABSTRACT FROM AUTHOR]

This article introduces a novel communication paradigm for the unsourced, uncoordinated Gaussian multiple access problem. The major components of the envisioned framework are as follows. The encoded bits of every message are partitioned into two groups. The first portion is transmitted using a compressive sensing scheme, whereas the second set of bits is conveyed using a multi-user coding scheme. The compressive sensing portion is key in sidestepping some of the challenges posed by the unsourced aspect of the problem. The information afforded by the compressive sensing is employed to create a sparse random multi-access graph conducive to joint decoding. This construction leverages the lessons learned from traditional IDMA into creating low-complexity schemes for the unsourced setting, while also accounting for inherent randomness. Under joint message-passing decoding, the proposed scheme offers good performance at a low computational complexity. Findings are supported by numerical simulations, and results are compared to existing alternatives. [ABSTRACT FROM AUTHOR]

Achieving reliable communications with low complexity is challenging for underwater acoustic communications with moving transceivers, where the time-varying channels need to be estimated and tracked accurately and data detection needs to be performed with low complexity. In this article, with the use of a superimposed training (ST) scheme, we address this challenge by developing a low-complexity channel estimation and tracking algorithm, which is then integrated with low-complexity data detection in the frequency domain. ST is used to acquire improved channel-tracking capability. Based on belief propagation, we design a message-passing-based low-complexity bidirectional channel estimation (LCE-MP) algorithm, where all computational intensive parts are handled by the fast Fourier transform (FFT) algorithm, thereby achieving very efficient implementation with logarithmic complexity. Specifically, a message-passing-based fast information collection algorithm is presented to acquire “local” channel estimates, followed by the fusion of local channel estimates to achieve a “global” estimate of the channel. It is shown that the computational complexity per channel tap is only in a logarithmic level for the channel estimation and tracking. Moreover, the ST-scheme-based LCE-MP algorithm is combined with FFT-based data detection and decoding, which are performed in an iterative manner. The overall complexity of channel estimation and data detection is in a logarithmic level, and the system delivers excellent performance thanks to the joint processing. Field experiments were carried out in Jiaozhou Bay in 2019, and the experimental results verify the effectiveness of the proposed technique. [ABSTRACT FROM AUTHOR]

This paper proposes a belief propagation (BP)-based algorithm for sequential detection and estimation of multipath component (MPC) parameters based on radio signals. Under dynamic channel conditions with moving transmitter/receiver, the number of MPCs, the MPC dispersion parameters, and the number of false alarm contributions are unknown and time-varying. We develop a Bayesian model for sequential detection and estimation of MPC dispersion parameters, and represent it by a factor graph enabling the use of BP for efficient computation of the marginal posterior distributions. At each time step, a snapshot-based parametric channel estimator provides parameter estimates of a set of MPCs which are used as noisy measurements by the proposed BP-based algorithm. It performs joint probabilistic data association, and estimation of the time-varying MPC parameters and the mean number of false alarm measurements, by means of the sum-product algorithm rules. The algorithm also exploits amplitude information enabling the reliable detection of “weak” MPCs with very low component signal-to-noise ratios (SNRs). The performance of the proposed algorithm compares well to state-of-the-art algorithms for high SNR MPCs, but it significantly outperforms them for medium or low SNR MPCs. Results using real radio measurements demonstrate the excellent performance of the proposed algorithm in realistic and challenging scenarios. [ABSTRACT FROM AUTHOR]

Knapsack problem (KP) is a representative combinatorial optimization problem that aims to maximize the total profit by selecting a subset of items under given constraints on the total weights. In this study, we analyze a generalized version of KP, which is termed the generalized multidimensional knapsack problem (GMDKP). As opposed to the basic KP, GMDKP allows multiple choices per item type under multiple weight constraints. Although several efficient algorithms are known and the properties of their solutions have been examined to a significant extent for basic KPs, there is a paucity of known algorithms and studies on the solution properties of GMDKP. To gain insight into the problem, we assess the typical achievable limit of the total profit for a random ensemble of GMDKP using the replica method. Our findings are summarized as follows: (1) when the profits of item types are normally distributed, the total profit grows in the leading order with respect to the number of item types as the maximum number of choices per item type x max increases while it depends on x max only in a sub-leading order if the profits are constant among the item types. (2) A greedy-type heuristic can find a nearly optimal solution whose total profit is lower than the optimal value only by a sub-leading order with a low computational cost. (3) The sub-leading difference from the optimal total profit can be improved by a heuristic algorithm based on the cavity method. Extensive numerical experiments support these findings. [ABSTRACT FROM AUTHOR]

The striking growth of powerful computing resources allows time-efficient solution of computationally demanding problems. In particular, advances in high-performance computing have made stochastic approaches to real-world applications more practical. The belief propagation (BP) algorithm is a probabilistic method typically used in information theory and artificial intelligence. This article exploits the probabilistic message passing attribute of BP for solving stochastic linear systems that naturally arise from finite element formulation of stochastic partial differential equations (PDEs), establishing an explicit connection between the two fields for the first time. The accuracy of the algorithm is validated by comparison to the well-known Monte Carlo method. [ABSTRACT FROM AUTHOR]

A system is considered in which agents (UAVs) must cooperatively discover interest-points (i.e., burning trees, geographical features) evolving over a grid. The objective is to locate as many interest-points as possible in the shortest possible time frame. There are two main problems: a control problem, where agents must collectively determine the optimal action, and a communication problem, where agents must share their local states and infer a common global state. Both problems become intractable when the number of agents is large. This survey/concept paper curates a broad selection of work in the literature pointing to a possible solution; a unified control/communication architecture within the framework of reinforcement learning. Two components of this architecture are locally interactive structure in the state-space, and hierarchical multi-level clustering for system-wide communication. The former mitigates the complexity of the control problem and the latter adapts to fundamental throughput constraints in wireless networks. The challenges of applying reinforcement learning to multi-agent systems are discussed. The role of clustering is explored in multi-agent communication. Research directions are suggested to unify these components. [ABSTRACT FROM AUTHOR]

Abstract Image interpolation has a wide range of applications such as frame rate-up conversion and free viewpoint TV. Despite significant progresses, it remains an open challenge especially for image pairs with large displacements. In this paper, we first propose a novel optimization algorithm for motion estimation, which combines the advantages of both global optimization and a local parametric transformation model. We perform optimization over dynamic label sets, which are modified after each iteration using the prior of piecewise consistency to avoid local minima. Then we apply it to an image interpolation framework including occlusion handling and intermediate image interpolation. We validate the performance of our algorithm experimentally, and show that our approach achieves state-of-the-art performance.

The Number Theoretic Transform (NTT) is a major building block in recently introduced lattice based post-quantum (PQ) cryptography. The NTT was target of a number of recently proposed Belief Propagation (BP)-based Side Channel Attacks (SCAs). Ravi et al. have recently proposed a number of countermeasures mitigating these attacks. In 2021, Hamburg et al. presented a chosen-ciphertext enabled SCA improving noise-resistance, which we use as a starting point to state our findings. We introduce a pre-processing step as well as a new factor node which we call shuffle node. Shuffle nodes allow for a modified version of BP when included into a factor graph. The node iteratively learns the shuffling permutation of fine shuffling within a BP run. We further expand our attacker model and describe several matching algorithms to find inter-layer connections based on shuffled measurements. Our matching algorithm allows for either mixing prior distributions according to a doubly stochastic mix matrix or to extract permutations and perform an exact un-matching of layers. We additionally discuss the usage of sub-graph inference to reduce uncertainty and improve un-shuffling of butterflies. Based on our results, we conclude that the proposed countermeasures of Ravi et al. are powerful and counter Hamburg et al., yet could lead to a false security perception – a powerful adversary could still launch successful attacks. We discuss on the capabilities needed to defeat shuffling in the setting of Hamburg et al. using our expanded attacker model. Our methods are not limited to the presented case but provide a toolkit to analyze and evaluate shuffling countermeasures in BP-based attack scenarios.

This work presents a data-driven factor graph (FG) model designed to perform anchor-based positioning. The system makes use of the FG to compute the target position, given the distance measurements to the anchor node that know its own position.The aim was to design a hybrid structure (that involves data and modeling approaches) to address positioning models from a Bayesian point of view, customizing them for each technology and scenario. The weighted geometric dilution of precision (WGDOP) metric, which measures the effect on the positioning solution of distance error to the corresponding anchor node and network geometry of the anchor nodes, was taken into account. The presented algorithms were tested with simulated data and also with real-life data collected from IEEE 802.15.4-compliant sensor network nodes with a physical layer based on ultra-wide band (UWB) technology, in scenarios with one target node, three and four anchor nodes, and a time-of-arrival-based range technique. The results showed that the presented algorithm based on the FG technique provided better positioning results than the least squares-based algorithms and even UWB-based commercial systems in various scenarios, with different setups in terms of geometries and propagation conditions.

This article investigates the performance of intelligent reflective surfaces (IRS)-aided physical layer network coding (PNC) in two-way relaying channels (TWRC). Specifically, IRS is used to eliminate carrier phase offset (CPO) at the relay node. To this end, the IRS reflectors’ phase shifts are optimized to align the received signals from two source nodes at the relay. This facilitates using a simple mapping function at the relay to map the superimposed signal to a network-coded signal. Two scenarios are considered, the first of which assumes that each source node is served by a separate IRS panel, while the second scenario considers the more challenging case where only one IRS panel is available for the two source nodes. In the latter case, the IRS panel is seen by both source nodes and its phase shifts are optimized to mitigate the CPO problem while maximizing the received signal amplitude at the relay. This optimization problem is formulated and solved over the complex circle manifold. Finally, we extend the IRS-assisted PNC system to include channel coding and higher modulation orders, for which a repeat accumulate (RA) channel-coded IRS-aided PNC scheme is proposed for general quadrature amplitude modulation (QAM) signals. A belief propagation (BP) based algorithm is designed to decode the network-coded sequences over a q-Ring using modular arithmetic. Our simulation results validate the theoretical error expressions derived for the two-IRS scenario as well as the efficacy of the proposed manifold optimization approach for the one-IRS scenario. The results also confirm the efficacy of the designed channel-coded IRS-aided PNC using high QAM modulation orders. [ABSTRACT FROM AUTHOR]

The Belief Propagation (BP) algorithm has the advantages of high-speed decoding and low latency. To improve the block error rate (BLER) performance of the BP-based algorithm, the BP flipping algorithm was proposed. However, the BP flipping algorithm attempts numerous useless flippings for improving the BLER performance. To reduce the number of decoding attempts needed without any loss of BLER performance, in this paper a metric is presented to evaluate the likelihood that the bits would correct the BP flipping decoding. Based on this, a BP-Step-Flipping (BPSF) algorithm is proposed which only traces the unreliable bits in the flip set (FS) to flip and skips over the reliable ones. In addition, a threshold β is applied when the magnitude of the log–likelihood ratio (LLR) is small, and an enhanced BPSF (EBPSF) algorithm is presented to lower the BLER. With the same FS, the proposed algorithm can reduce the average number of iterations efficiently. Numerical results show the average number of iterations for EBPSF-1 decreases by 77.5% when N = 256, compared with the BP bit-flip-1 (BPF-1) algorithm at E b / N 0 = 1.5 dB. [ABSTRACT FROM AUTHOR]

The probabilistic forecasting method described in this study is devised to leverage spatial and temporal dependency of urban traffic networks, in order to provide predictions accurate at short term and meaningful for a horizon of up to several hours. By design, it can deal with missing data, both for training and running the model. It is able to forecast the state of the entire network in one pass, with an execution time that scales linearly with the size of the network. The method consists in learning a sparse Gaussian copula of traffic variables, compatible with the Gaussian belief propagation algorithm. The model is trained automatically from an historical dataset through an iterative proportional scaling procedure, that is well suited to compatibility constraints induced by Gaussian belief propagation. Results of tests performed on two urban datasets show a very good ability to predict flow variables and reasonably good performances on speed variables. Some understanding of the observed performances is given by a careful analysis of the model, making it to some degree possible to disentangle modeling bias from the intrinsic noise of the traffic phenomena and its measurement process. [ABSTRACT FROM AUTHOR]

Message propagation algorithms from statistical physics are widely used in various fields of artificial intelligence, especially in solving combinatorial optimization problems. According to the related literatures of message propagation algorithm, this paper summarized the history of message propagation algorithm and its related application. According to the development of message propagation algorithm, it introduced the concepts of information propagation algorithm for solving the satisfiability problem, which mainly involved the warning propagation algorithm, the belief propagation algorithm and the survey propagation algorithm. This paper described the convergence and effectiveness research of the three kind of algorithms, summarized the application of each algorithm in related fields, and summarized the research path and application direction of message propagation algorithm. [ABSTRACT FROM AUTHOR]

Belief propagation (BP) is widely used to solve the cooperative localization problem due to its excellent performance and natural distributed structure of implementation. For a mobile agent network, its factor graph inevitably encounters loops. In this case, the BP algorithm becomes iterative and can only provide an approximate marginal probability density function of the estimate with finite iterations. We propose an augmented-state BP algorithm for mobile agent networks to alleviate the effect of loops. By performing state augmentation, the messages in the factor graph will actually be allowed to be backward propagated, which reduces the number of loops in the factor graph, increases the available information of agents, and thus, benefits the localization. Experimental results demonstrate the better performance of the proposed algorithm over the original BP method. [ABSTRACT FROM AUTHOR]

This letter incorporates the adaptive kernel Kalman filter (AKKF) into the belief propagation (BP) algorithm for Multi-target tracking (MTT) in single-sensor systems. The algorithm is capable of tracking an unknown and time-varying number of targets, in the presence of false alarms, clutter and measurement-to-target association uncertainty. Experiment results reveal that the proposed method has a favourable tracking performance using the generalized optimal sub-patten assignment (GOSAP) metrics at substantially less computation cost than the particle filter (PF) based Multi-target tracking (MTT) BP algorithm. [ABSTRACT FROM AUTHOR]

As an algorithmic framework, message passing is extremely powerful and has wide applications in the context of different disciplines including communications, coding theory, statistics, signal processing, artificial intelligence and combinatorial optimization. In this paper, we investigate the performance of a message-passing algorithm called min-sum belief propagation (BP) for the vertex-disjoint shortest $k$ -path problem ($k$ -VDSP) on weighted directed graphs, and derive the iterative message-passing update rules. As the main result of this paper, we prove that for a weighted directed graph $G$ of order $n$ , BP algorithm converges to the unique optimal solution of $k$ -VDSP on $G$ within $O(n^{2}w_{max})$ iterations, provided that the weight $w_{e}$ is nonnegative integral for each arc $e\in E(G)$ , where $w_{max}=\max \{w_{e}: e\in E(G)\}$. To the best of our knowledge, this is the first instance where BP algorithm is proved correct for NP-hard problems. Additionally, we establish the extensions of $k$ -VDSP to the case of multiple sources or sinks. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of learning a tree graph structure that represents the statistical data dependency among nodes for a set of data samples generated by nodes, which provides the basic structure to perform a probabilistic inference task. Inference in the data graph includes marginal inference and maximum a posteriori (MAP) estimation, and belief propagation (BP) is a commonly used algorithm to compute the marginal distribution of nodes via message-passing, incurring non-negligible amount of communication cost. We inevitably have the trade-off between the inference accuracy and the message-passing cost because the learned structure of data dependency and physical connectivity graph are often highly different. In this paper, we formalize this trade-off in an optimization problem which outputs the data dependency graph that jointly considers learning accuracy and message-passing costs. We focus on two popular implementations of BP, ASYNC-BP and SYNC-BP, which have different message-passing mechanisms and cost structures. In ASYNC-BP, we propose a polynomial-time learning algorithm that is optimal, motivated by finding a maximum weight spanning tree of a complete graph. In SYNC-BP, we prove the NP-hardness of the problem and propose a greedy heuristic. For both BP implementations, we quantify how the error probability that the learned cost-efficient data graph differs from the ideal one decays as the number of data samples grows, using the large deviation principle, which provides a guideline on how many samples are necessary to obtain a certain trade-off. We validate our theoretical findings through extensive simulations, which confirms that it has a good match. [ABSTRACT FROM AUTHOR]

Abstract Clock synchronization has always been a major challenge when designing wireless networks. This work focuses on tackling the time synchronization problem in 5G networks by adopting a hybrid Bayesian approach for clock offset and skew estimation. Furthermore, we provide an in-depth analysis of the impact of the proposed approach on a synchronization-sensitive service, i.e., localization. Specifically, we expose the substantial benefit of belief propagation (BP) running on factor graphs (FGs) in achieving precise network-wide synchronization. Moreover, we take advantage of Bayesian recursive filtering (BRF) to mitigate the time-stamping error in pairwise synchronization. Finally, we reveal the merit of hybrid synchronization by dividing a large-scale network into local synchronization domains and applying the most suitable synchronization algorithm (BP- or BRF-based) on each domain. The performance of the hybrid approach is then evaluated in terms of the root mean square errors (RMSEs) of the clock offset, clock skew, and the position estimation. According to the simulations, in spite of the simplifications in the hybrid approach, RMSEs of clock offset, clock skew, and position estimation remain below 10 ns, 1 ppm, and 1.5 m, respectively.

• The multi-model belief propagation method implemented using Gaussian mixture. • Extracting sensor association measurements using greedy algorithm. • Fusion of all association measurements using the belief iterative updating algorithm. • Multisensor-multitarget three-dimensional tracking simulation scenario with clutter. This paper presents a novel Gaussian mixture multi-model belief propagation (GMM-BP) filter for maneuvering multitarget tracking with multiple sensors. The filter is built upon the BP-based multisensor-multitarget tracking scheme, enabling accurate estimation of target numbers and states. It assumes linear Gaussian target motion, birth process, and sensor measurement models and utilizes the Gaussian mixture model to represent the target's marginal posterior probability density function. Additionally, multiple models are incorporated for target motion, enhancing the maneuvering target tracking capability. Closed-form recursions for means, covariances, and weights of Gaussian components in the filter are derived. To enhance scalability with the number of sensors, a simplified multi-sensor data fusion process is proposed, thereby preserving a linear relationship between Gaussian components and the number of sensors. The performance of the filter was evaluated in a three-dimensional (3D) simulated environment. Compared to the particle-implemented BP filter and the labeled multi-Bernoulli (LMB) filter, the proposed GMM-BP filter exhibited a significant improvement in computational efficiency while maintaining a high level of tracking accuracy. [ABSTRACT FROM AUTHOR]

Worries about the increasing number of Sybils in online social networks (OSNs) are amplified by a range of security issues; thus, Sybil detection has become an urgent real-world problem. Lightweight and limited data-friendly, LBP (Loopy Belief Propagation)-based Sybil-detection methods on the social graph are extensively adopted. However, existing LBP-based methods that do not utilize node attributes often assume a global or predefined homophily strength of edges in the social graph, while different user’s discrimination and preferences may vary, resulting in local homogeneity differences. Another issue is that the existing message-passing paradigm uses the same edge potential when propagating belief to both sides of a directed edge, which does not agree with the trust interaction in one-way social relationships. To bridge these gaps, we present SybilHP, a Sybil-detection method optimized for directed social networks with adaptive homophily prediction. Specifically, we incorporate an iteratively updated edge homophily estimation into the belief propagation to better adapt to the personal preferences of real-world social network users. Moreover, we endow message passing on edges with directionality by a direction-sensitive potential function design. As a result, SybilHP can better capture the local homophily and direction pattern in real-world social networks. Experiments show that SybilHP works with high detection accuracy on synthesized and real-world social graphs. Compared with various state-of-the-art graph-based methods on a large-scale Twitter dataset, SybilHP substantially outperforms existing methods.

This paper proposes a centralized MTT method based on a state-of-the-art multi-sensor labeled multi-Bernoulli (LMB) filter in underwater multi-static networks with autonomous underwater vehicles (AUVs). The LMB filter can accurately extract the number of targets and trajectories from measurements affected by noise, missed detections, false alarms and port–starboard ambiguity. However, its complexity increases as the number of sensors increases. In addition, due to the time-varying underwater environment, AUV detection probabilities are time-varying, and their mismatches often lead to poor MTT performance. Consequently, we detail a robust multi-sensor LMB filter that estimates detection probabilities and multi-target states simultaneously in real time. Moreover, we derive an effective approximate form of the multi-sensor LMB filter using Kullback–Leibler divergence and develop an efficient belief propagation (BP) implementation of the multi-sensor LMB filter. Our method scales linearly with the number of AUVs, providing good scalability and low computational complexity. The proposed method demonstrates superior performance in underwater multi-AUV network MTT simulations.

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Online social networks are vital environments for information sharing and user interactivity. To help users of online social services to build, expand, and maintain their friend networks or webs of trust, trust management systems have been deployed and trust inference (or more generally, friend recommendation) techniques have been studied in many online social networks. However, there are some challenging issues obstructing the real-world trust inference tasks. Using only explicit yet sparse trust relationships to predict user trust is inefficient in large online social networks. In the age of privacy-respecting Internet, certain types of user data may be unavailable, and thus existing models for trust inference may be less accurate or even defunct. Although some less interpretable models may achieve better performance in trust prediction, the interpretability of the models may prevent them from being adopted or improved for making relevant informed decisions. To tackle these problems, we propose a probabilistic graphical model for trust inference in online social networks in this paper. The proposed model is built upon the skeleton of explicit trust relationships (the web of trust) and embeds various types of available user data as comprehensively-designed trust-aware features. A message passing algorithm, loop belief propagation, is applied to the model inference, which greatly improves the interpretability of the proposed model. The performance of the proposed model is demonstrated by experiments on a real-world online social network dataset. Experimental results show the proposed model achieves acceptable accuracy with both fully and partially available data. Comparison experiments were conducted, and the results show the proposed model's promise for trust inference in some circumstances. [ABSTRACT FROM AUTHOR]

As navigation is a key to task execution of micro unmanned aerial vehicle (UAV) swarm, the cooperative navigation (CN) method that integrates relative measurements between UAVs has attracted widespread attention due to its performance advantages. In view of the precision and efficiency of cooperative navigation for low-cost micro UAV swarm, this paper proposes a sigma point belief propagation-based (SPBP) CN method that can integrate self-measurement data and inter-UAV ranging in a distributed manner so as to improve the absolute positioning performance of UAV swarm. The method divides the sigma point filter into two steps: the first is to integrate local measurement data; the second is to approximate the belief of position based on the mean and covariance of the state, and pass message by augmentation, resampling and cooperative measurement update of the state to realize a low-complexity approximation to traditional message passing method. The simulation results and outdoor flight test results show that with similar performance, the proposed CN method has a calculation load more than 20 times less than traditional BP algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a joint device activity detection (DAD), channel estimation (CE), and data decoding (DD) algorithm for multiple-input multiple-output (MIMO) massive unsourced random access (URA). Different from the state-of-the-art slotted transmission scheme, the data in the proposed framework is split into only two parts. A portion of the data is coded by compressed sensing (CS) and the rest is low-density-parity-check (LDPC) coded. In addition to being part of the data, information bits in the CS phase also undertake the task of interleaving pattern design and CE. The principle of interleave-division multiple access (IDMA) is exploited to reduce the interference among devices in the LDPC phase. Based on the belief propagation (BP) algorithm, a low-complexity iterative message passing (MP) algorithm is utilized to decode the data embedded in these two phases separately. Moreover, combined with successive interference cancellation (SIC), the proposed joint DAD-CE-DD algorithm is performed to further improve performance by utilizing the belief of each other. Additionally, based on the energy detection (ED) and sliding window protocol (SWP), we develop a collision resolution protocol to handle the codeword collision, a common issue in the URA system. In addition to the complexity reduction, the proposed algorithm exhibits a substantial performance enhancement compared to the state-of-the-art in terms of efficiency and accuracy. [ABSTRACT FROM AUTHOR]

Only a specific location can make sensor data useful. The paper presents an simplify belief propagation and variation expectation maximization (SBPVEM) algorithm to achieve node localization by cooperating with another target node while lowering communication costs in a challenging environment where the anchor is sparse. A simplified belief propagation algorithm is proposed as the overall reasoning framework by modeling the cooperative localization problem as a graph model. The high-aggregation sampling and variation expectation–maximization algorithm is applied to sample and fit the complicated distribution. Experiments show that SBPVEM can obtain accurate node localization equal to NBP and SPAWN in a challenging environment while reducing bandwidth requirements. In addition, the SBPVEM has a better expressive ability than PVSPA, for SBPVEM is efficient in challenging environments. [ABSTRACT FROM AUTHOR]

Despite the great promises that the resistive random access memory (ReRAM) has shown as the next generation of non-volatile memory technology, its crossbar array structure leads to a severe sneak path interference to the signal read back from the memory cell. In this paper, we first propose a novel belief propagation (BP) based detector for the sneak path interference in ReRAM. Based on the conditions for a sneak path to occur and the dependence of the states of the memory cells that are involved in the sneak path, a Tanner graph for the ReRAM channel is constructed, inside which specific messages are updated iteratively to get a better estimation of the sneak path affected cells. We further combine the graph of the designed BP detector with that of the BP decoder of the polar codes to form a joint detector and decoder. Tailored for the joint detector and decoder over the ReRAM channel, effective polar codes are constructed using the genetic algorithm. Simulation results show that the BP detector can effectively detect the cells affected by the sneak path, and the proposed polar codes and the joint detector and decoder can significantly improve the error rate performance of ReRAM. [ABSTRACT FROM AUTHOR]

Turbo Product Codes (TPCs) with near Shannon capacity performance especially for high code rates allow decoding operations at a speed of several Mbps or more and has low error floor. In this paper, the characteristics of TPCs as FEC code, and their suitability for being a potential candidate in satellite communication applications are discussed. Emphasis is laid to highlight TPC's flexibility in attaining different code rates, improved coding gain especially for high code rates, benefits of different types of TPC construction and decoding modifications. The need for careful amalgamation of higher order modulation techniques with TPCs to achieve good spectral and power efficiency is discussed in detail. In addition, a deep learning based approach (neural-BP) for decoding of TPCs has been discussed. This is intended to improve the parallelism in TPC decoding and to reduce the implementation complexity. It is envisaged that the neural-BP decoding approach would lead to the design of hardware efficient decoders providing high Quality of Service (QoS) in satellite communication systems. [ABSTRACT FROM AUTHOR]

In order to reduce the decoding latency, a new early stopping criterion is proposed for belief propagation (BP) decoding. A kind of special processing elements (PEs) of BP decoder called frozen and information PE (FIPE) is selected to predict whether decoding is successful or not. Statistics indicate that FIPE can be considered reliable when the frozen bit is decoded successfully. The proposed criterion is based on the fact that the number of reliable FIPE increase along with iterations and the variation trend is approximate to the ratio of correct information bits. In the term of hardware complexity, the proposed method has a linear correlation with the number of FIPE. This criterion consumes only ‘xor’ and ‘or’ gates to check stopping condition. Simulation results show that the proposed criterion achieves lower latency than Worst Information Bits (WIB) and Frozen Bit Error Rate (FBER) without BLER degradation compared with fixed and G-matrix. Compared with WIB, FBER, Best Frozen Bits (BFB) criterion, the proposed criterion has the lowest hardware complexity. [ABSTRACT FROM AUTHOR]

In this paper, we propose a channel reliability compensation factor to enhance the performance of belief propagation polar decoders on flat Rayleigh fading channels with noisy channel estimates. By including the error variance of the channel estimate in the derivation of the channel intrinsic information, the formula for calculating the value of channel reliability compensation factor is provided. Simulation results show that a BP polar decoder with the proposed compensation factor achieves a gain of 1.5 dB at a BER of 10 - 3 compared to the one without using the compensation factor. This gain is obtained with no additional complexity. [ABSTRACT FROM AUTHOR]

A novel fault-tolerant computation technique based on array Belief Propagation (BP)-decodable XOR (BP-XOR) codes is proposed for distributed matrix-matrix multiplication. The proposed scheme is shown to be configurable and suited for modern hierarchical compute architectures such as Graphical Processing Units (GPUs) equipped with multiple nodes, whereby each has many small independent processing units with increased core-to-core communications. The proposed scheme is shown to outperform a few of the well–known earlier strategies in terms of total end-to-end execution time while in presence of slow nodes, called stragglers. This performance advantage is due to the careful design of array codes which distributes the encoding operation over the cluster (slave) nodes at the expense of increased master-slave communication. An interesting trade-off between end-to-end latency and total communication cost is precisely described. In addition, to be able to address an identified problem of scaling stragglers, an asymptotic version of array BP-XOR codes based on projection geometry is proposed at the expense of some computation overhead. A thorough latency analysis is conducted for all schemes to demonstrate that the proposed scheme achieves order-optimal computation in both the sublinear as well as the linear regimes in the size of the computed product from an end-to-end delay perspective. [ABSTRACT FROM AUTHOR]

Simultaneous localization and mapping (SLAM) during communication is emerging. This technology promises to provide information on propagation environments and transceivers’ location, thus creating several new services and applications for the Internet of Things and environment-aware communication. Using crowdsourcing data collected by multiple agents appears to be much potential for enhancing SLAM performance. However, the measurement uncertainties in practice and biased estimations from multiple agents may result in serious errors. This study develops a robust SLAM method with measurement plug-and-play and crowdsourcing mechanisms to address the above problems. First, we divide measurements into different categories according to their unknown biases and realize a measurement plug-and-play mechanism by extending the classic belief propagation (BP)-based SLAM method. The proposed mechanism can obtain the time-varying agent location, radio features, and corresponding measurement biases (such as clock bias, orientation bias, and received signal strength model parameters), with high accuracy and robustness in challenging scenarios without any prior information on anchors and agents. Next, we establish a probabilistic crowdsourcing-based SLAM mechanism, in which multiple agents cooperate to construct and refine the radio map in a decentralized manner. Our study presents the first BP-based crowdsourcing that resolves the “double count” and “data reliability” problems through the flexible application of probabilistic data association methods. Numerical results reveal that the crowdsourcing mechanism can further improve the accuracy of the mapping result, which, in turn, ensures the decimeter-level localization accuracy of each agent in a challenging propagation environment. [ABSTRACT FROM AUTHOR]

The low-density parity-check (LDPC) code is chosen to be a channel coding scheme for ultra-reliable and low-latency communications (URLLC) in 5G. However, short LDPC compromises the belief propagation (BP) decoder because of the stopping set (SS) in the decoding graph. We propose to perform different node operations according to the SS. A dynamic SS selection according to the received sequence is further applied. In the length 308 5G LDPC, our decoders outperform prior work, improving BP and collaborative BP at block error probability (BLER) of $3 \times 10^{-5}$ by around 0.176 and 0.092 dB respectively, with almost the same number of iterations. [ABSTRACT FROM AUTHOR]

Image interpolation has a wide range of applications such as frame rate-up conversion and free viewpoint TV. Despite significant progresses, it remains an open challenge especially for image pairs with large displacements. In this paper, we first propose a novel optimization algorithm for motion estimation, which combines the advantages of both global optimization and a local parametric transformation model. We perform optimization over dynamic label sets, which are modified after each iteration using the prior of piecewise consistency to avoid local minima. Then we apply it to an image interpolation framework including occlusion handling and intermediate image interpolation. We validate the performance of our algorithm experimentally, and show that our approach achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]