In this paper, a novel soft reliability-based iterative majority-logic decoding algorithm with uniform quantization is proposed for regularly structured low density parity-check(LDPC) codes. A weighted measure is intr...In this paper, a novel soft reliability-based iterative majority-logic decoding algorithm with uniform quantization is proposed for regularly structured low density parity-check(LDPC) codes. A weighted measure is introduced for each check-sum of the parity-check matrix and a scaling factor is used to weaken the overestimation of extrinsic information. Furthermore, the updating process of the reliability measure takes advantage of turbo-like iterative decoding strategy. The main computational complexity of the proposed algorithm only includes logical and integer operations with the bit uniform quantization criterion. Simulation results show that the novel decoding algorithm can achieve excellent error-correction performance and a fast decoding convergence speed.展开更多
This paper conducts a survey on iterative learn-ing control(ILC)with incomplete information and associated control system design,which is a frontier of the ILC field.The incomplete information,including passive and ac...This paper conducts a survey on iterative learn-ing control(ILC)with incomplete information and associated control system design,which is a frontier of the ILC field.The incomplete information,including passive and active types,can cause data loss or fragment due to various factors.Passive incomplete information refers to incomplete data and information caused by practical system limitations during data collection,storage,transmission,and processing,such as data dropouts,delays,disordering,and limited transmission bandwidth.Active incomplete information refers to incomplete data and information caused by man-made reduction of data quantity and quality on the premise that the given objective is satisfied,such as sampling and quantization.This survey emphasizes two aspects:the first one is how to guarantee good learning performance and tracking performance with passive incomplete data,and the second is how to balance the control performance index and data demand by active means.The promising research directions along this topic are also addressed,where data robustness is highly emphasized.This survey is expected to improve understanding of the restrictive relationship and trade-off between incomplete data and tracking performance,quantitatively,and promote further developments of ILC theory.展开更多
基金supported by the National Natural Science Foundation of China(Nos.61472464,61671091 and 61471075)the Natural Science Foundation of Chongqing Science and Technology Commission(No.cstc2015jcyj A0554)+1 种基金the Program for Innovation Team Building at Institutions of Higher Education in Chongqing(No.J2013-46)the Undergraduate Science Research Training Project for Chongqing University of Posts and Telecommunications(No.A2016-61)
文摘In this paper, a novel soft reliability-based iterative majority-logic decoding algorithm with uniform quantization is proposed for regularly structured low density parity-check(LDPC) codes. A weighted measure is introduced for each check-sum of the parity-check matrix and a scaling factor is used to weaken the overestimation of extrinsic information. Furthermore, the updating process of the reliability measure takes advantage of turbo-like iterative decoding strategy. The main computational complexity of the proposed algorithm only includes logical and integer operations with the bit uniform quantization criterion. Simulation results show that the novel decoding algorithm can achieve excellent error-correction performance and a fast decoding convergence speed.
基金supported by the National Natural Science Foundation of China(61673045)Beijing Natural Science Foundation(4152040)
文摘This paper conducts a survey on iterative learn-ing control(ILC)with incomplete information and associated control system design,which is a frontier of the ILC field.The incomplete information,including passive and active types,can cause data loss or fragment due to various factors.Passive incomplete information refers to incomplete data and information caused by practical system limitations during data collection,storage,transmission,and processing,such as data dropouts,delays,disordering,and limited transmission bandwidth.Active incomplete information refers to incomplete data and information caused by man-made reduction of data quantity and quality on the premise that the given objective is satisfied,such as sampling and quantization.This survey emphasizes two aspects:the first one is how to guarantee good learning performance and tracking performance with passive incomplete data,and the second is how to balance the control performance index and data demand by active means.The promising research directions along this topic are also addressed,where data robustness is highly emphasized.This survey is expected to improve understanding of the restrictive relationship and trade-off between incomplete data and tracking performance,quantitatively,and promote further developments of ILC theory.
