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16th IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (IEEE BMSB)

作者： Zhou, Ziqi Peng, Kewu Song, Jian He, Zhitong Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Elect Engn Dept Beijing 100084 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China

ISBN: (纸本)9781665449083

belief propagation (BP) provides the basis for the design of 5G new radio low density parity check (5G-NR LDPC) codes, which in turn require a BP-based decoding algorithm. The min-sum algorithm (MSA), the simplification of sum-product algorithm (SPA), should hardly be regarded as a BP-based decoding algorithm because of its minimization. To remedy this, this paper adopts the multi-edge-type density evolution (MET-DE) as the main analysis tool, and introduces the concept of Kullback-Leibler divergence. Then the least Kullback-Leibler divergence criterion is proposed to identify the optimal normalization factors. Finally, Density-Evolution-aided Adaptive Normalization Min-Sum algorithm (DE-aided ANMSA) with edge classification is further proposed for the decoding implementation of 5G-NR LDPC codes. Simulation results demonstrate that the proposed decoding algorithm can achieve excellent performance, while the greatest advantage is that its factors could be obtained more flexibly and accurately than traditional training methods. In addition, the optimal factors for base graph 2 in 5G-NR LDPC codes are provided as well.

关键词： 5G new radio low-density parity check codes iterative decoding algorithm belief propagation algorithm multi-edge-type density evolution Kullback-Leibler divergence adaptive normalization min-sum algorithm

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Underwater Target Threat Assessment Method Based on Bayesian Network 40

Underwater Target Threat Assessment Method Based on Bayesian...

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40th Chinese Control Conference (CCC)

作者： Li, Danyi Liu, Meiqin Zhang, Senlin Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China

ISBN: (纸本)9789881563804

Underwater targets are used more and more frequently in modern ocean battlefield. It is important to assess the threat from an enemy target after it is detected immediately, in order to prepare quick counterattack and defense. This paper proposes a method for underwater target threat assessment based on Bayesian Network. The threat model uses a hierarchy view for threat factors. All factors are divided into three parts: environment, target space and target fire. Each part contains several detailed factors, which are arranged based on their causal relationships. These factors are applied into the Bayesian Network and the probability of threat is calculated through Bayesian inference. Finally, a simulation is conducted and the method can effectively assess the target threat. The model has a relatively good performance and can generate the assessment with low time cost.

关键词： Underwater target threat assessment situation awareness Bayesian Network belief propagation algorithm

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Enhanced Adaptive Normalized Min-Sum algorithm for Layered Scheduling of 5G-NR LDPC codes 15

Enhanced Adaptive Normalized Min-Sum Algorithm for Layered S...

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15th IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Zhou, Ziqi Peng, Kewu Krylov, Aleksei Rashich, Andrey Tkachenko, Dmitry Li, Fengshuang Zhang, Chao Song, Jian Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Elect Engn Dept Beijing 100084 Peoples R China Peter Great St Petersburg Polytech Univ St Petersburg Russia

ISBN: (纸本)9781728157849

5G new radio low density parity check (5G-NR LDPC) codes specified by the 3rd generation partnership project. (3GPP), a representative class of multi-edge-type, quasi-cyclic and raptor-like LDPC codes with rate-compatible and length scalable capability, could meet with high-throughput demands. The corresponding decoding algorithm and implementation should support low complexity, high performance and scale invariance to channel variations and receiver imperfections. Flooding scheduling is usually used for the prevalent decoding algorithms and architecture of LDPC codes, but it is inefficient and inflexible compared with layered scheduling This paper proposes a new simplification method where the sub-groups with similar fitting functions of normalization factors are incorporated into the same type, and enhanced adaptive normalized min-sum algorithm (enhanced ANMSA) for layered scheduling is further proposed for the implementation of 5G-NR LDPC codes. Based on the new simplification method, the proposed algorithm defines three types of check nodes for base graph 2 (BC2) in 5G-NR LDPC codes, and then obtains three look-up tables for BG2 by training Simulation results demonstrate that the proposed algorithm can achieve excellent performance for 5G-NR LDPC codes of different rates and lengths over Binary-input AWGN channel and Rayleigh channel.

关键词： 5G new radio low-density parity check codes iterative decoding algorithm belief propagation algorithm enhanced adaptive normalized min-sum algorithm for layered scheduling

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A Physical Probabilistic Network Model for Distribution Network Topology Recognition Using Smart Meter Data

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IEEE TRANSACTIONS ON SMART GRID 2019年第6期10卷 6965-6973页

作者： Jiang, Wei Chen, Jinming Tang, Haibo Cheng, Shu Hu, Qinran Cai, Mengmeng Rahman, Saifur Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Virginia Tech Adv Res Inst Arlington VA 22203 USA

Given the considerable scale of distribution networks in urban and rural areas, as well as the lack of management records, adjustments of switches during the distribution system operation are poorly documented. Such deficiency results in the inaccuracy of models stored in the distribution network automation system, and thus misleads the state estimation. With the emergence of information and communication technology, a large number of the feeder and residential smart meter data are accumulated. Such data can help recognize the operation modes of distribution networks by analyzing the relationships between the on/off states of switches and the voltage correlations among buses. However, the limited quantity and quality of the sampling data restrict the implementation of data-driven recognition. In this paper, a physical-probabilistic-network (PPN) model applied for inferring overall operation mode of distribution networks is proposed. Based on which, a belief propagation-based algorithm is proposed for the inference even under situations when there are only partial bus voltages data available. Meanwhile, the required variable for inference can be reduced from the active trail analysis. Experiment results are used to compare its performance with classic methods and to prove its effectiveness and advantages.

关键词： Distribution network topology recognition probabilistic graphical model belief propagation algorithm

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Uncertain Knowledge Reasoning Based on the Fuzzy Multi Entity Bayesian Networks

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Computers, Materials & Continua 2019年第7期61卷 301-321页

作者： Dun Li Hong Wu Jinzhu Gao Zhuoyun Liu Lun Li Zhiyun Zheng Zhengzhou University 100 Science AvenueZhengzhou450001China University of the Pacific 3601 Pacific AvenueStockton95211USA

With the rapid development of the semantic web and the ever-growing size of uncertain data,representing and reasoning uncertain information has become a great challenge for the semantic web application *** this paper,we present a novel reasoning framework based on the representation of fuzzy ***,the paper gives an overview of the previous research work on uncertainty knowledge representation and reasoning,incorporates Ontology into the fuzzy Multi Entity Bayesian Networks theory,and introduces fuzzy PR-OWL,an Ontology language based on *** PROWL describes fuzzy semantics and uncertain relations and gives grammatical definition and semantic ***,the paper explains the integration of the Fuzzy Probability theory and the belief propagation *** influencing factors of fuzzy rules are added to the belief that is propagated between the nodes to create a reasoning framework based on fuzzy *** that,the reasoning process,including the SSFBN structure algorithm,data fuzzification,reasoning of fuzzy rules,and fuzzy belief propagation,is ***,compared with the classical algorithm from the aspect of accuracy and time complexity,our uncertain data representation and reasoning method has higher accuracy without significantly increasing time complexity,which proves the feasibility and validity of our solution to represent and reason uncertain information.

关键词： Ontology language uncertainty representation uncertainty reasoning fuzzy multi entity Bayesian networks belief propagation algorithm fuzzy PR-OWL

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Underwater Target Threat Assessment Method Based on Bayesian Network

Underwater Target Threat Assessment Method Based on Bayesian...

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第40届中国控制会议

作者： Danyi Li Meiqin Liu Senlin Zhang State Key Laboratory of Industry Control Technology Zhejiang University College of Electrical Engineering Zhejiang University

Underwater targets are used more and more frequently in modern ocean *** is important to assess the threat from an enemy target after it is detected immediately,in order to prepare quick counterattack and *** paper proposes a method for underwater target threat assessment based on Bayesian *** threat model uses a hierarchy view for threat *** factors are divided into three parts:environment,target space and target *** part contains several detailed factors,which are arranged based on their causal *** factors are applied into the Bayesian Network and the probability of threat is calculated through Bayesian ***,a simulation is conducted and the method can effectively assess the target *** model has a relatively good performance and can generate the assessment with low time cost.

关键词： Underwater target threat assessment situation awareness Bayesian Network belief propagation algorithm

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Network calibration method based on a Bayesian approach using frequency modulation communications for wireless systems over additive white Gaussian noise channel

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IET SIGNAL PROCESSING 2018年第4期12卷 454-462页

作者： Chaal, Dina Lyhyaoui, Abdelouahid ENSAT LTI Lab BP 1818 Tangier Principal Tangier Morocco

Wireless sensor networks offer flexibility to monitor environmental conditions. However, in some hostile environments with changing propagation occurrences, it is challenging to guarantee a network free from systematic errors (biases), those errors can corrupt the data gathered from the network. The authors intend to address this problem using a Bayesian approach. This study presents a new Bayesian wireless network model based on a belief propagation algorithm over a noisy communication, to seek the calibration of the network for correlated sources. They set up a point-to-point communication model, consisting of a frequency modulation signal at the transmitter and a discrete-time phase-locked loop structure at the receiver over an additive white Gaussian noise channel. Then, they use a Bayesian approach to model the propagation of information, and analyse the noise impact on their system. The simulation results show significant improvement within a few time instants in the mean squared error of the sensors internal states. An evaluation in a noisy environment confirms the robustness of the proposed system.

关键词： calibration Bayes methods frequency modulation AWGN channels wireless channels radiowave propagation radio transmitters phase locked loops radio receivers mean square error methods mean squared error method receiver discrete-time phase-locked loop structure transmitter point-to-point communication model belief propagation algorithm environmental condition monitoring wireless sensor network additive white Gaussian noise channel frequency modulation communication Bayesian approach network calibration method

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Polar Code and Polarization using Bhattacharya Parameter 9

Polar Code and Polarization using Bhattacharya Parameter

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9th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)

作者： Mude, Shoban Bhukya, RajendraNaik Osmania Univ Univ Coll Engn Dept Elect & Commun Engn Hyderabad Telangana India

ISBN: (纸本)9781538672662

Performance test of a communication channel generally measured using bit error rate (BER) or signal to noise ratio (SNR) over various channels AWGN, BEC, BSC and BI-DMC. To improve channel capacity further by enhanced belief propagation algorithm for polar codes over BI-DMC it has better response, lower complexity at receiver by using Bhattacharya parameter Z (W)-factor it reaches near to Shannon's capacity.

关键词： BER SNR Polar Codes channel capacity belief propagation algorithm

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Iterative Soft-Decision Decoding of Binary Cyclic Codes based on Extended Parity-Check Transformation algorithm

Iterative Soft-Decision Decoding of Binary Cyclic Codes base...

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IEEE Canadian Conference on Electrical & Computer Engineering (CCECE)

作者： Babalola, Oluwaseyi Versfeld, Jaco Stellenbosch Univ Dept Elect & Elect Engn ZA-7600 Stellenbosch South Africa

ISBN: (纸本)9781538624104

In this paper, an iterative soft-decision (SD) decoding algorithm for cyclic codes based on extended parity-check equations is developed. The algorithm does not necessarily utilize the algebraic properties of the code, but operates on transforming the systematic parity-check matrix using the soft reliability information matrix obtained from the received vector. Results show a significant performance gain when compared with the hard decision Berlekamp-Massey (B-M) and belief propagation (BP) algorithms, but present a similar bit error rate (BER) performance when compared to the adaptive belief propagation (ABP) algorithm. An important feature of the decoder is that it functions within a practical decoding time complexity, and can be generally implemented for the class of linear block codes.

关键词： Soft-decision decoding Cyclic codes Extended parity-check equations Berlekamp-Massey algorithm belief propagation algorithm Adaptive belief propagation algorithm

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Node Selection for belief propagation Based Channel Equalization

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IEICE TRANSACTIONS ON COMMUNICATIONS 2017年第8期E100B卷 1285-1292页

作者： Hagiwara, Mitsuyoshi Nishimura, Toshihiko Ohgane, Takeo Ogawa, Yasutaka Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Hokkaido 0600814 Japan

Recently, much progress has been made in the study of belief propagation (BP) based signal detection with large-scale factor graphs. When we apply the BP algorithm to equalization in a SISO multipath channel, the corresponding factor graph has many short loops and patterns in an edge connection/strength. Thus, proper convergence may not be achieved. In general, the log-likelihood ratio (LLR) oscillates in ill-converged cases. Therefore, LLR oscillation avoidance is important for BP-based equalization. In this paper, we propose applying node selection (NS) to prevent the LLR from oscillating. The NS extends the loop length virtually by a serial LLR update. Thus, some performance improvement is expected. Simulation results show that the error floor is significantly reduced by NS in the uncoded case and that the NS works very well in the coded case.

关键词： belief propagation algorithm log-likelihood ratio interference canceller equalization

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