This paper proposes a scheme to construct orthogonal channel matrix for full rate quasiorthogonal STBC based on givens rotation with lower bit error rate. The transmission diversity method rotates every single informa...This paper proposes a scheme to construct orthogonal channel matrix for full rate quasiorthogonal STBC based on givens rotation with lower bit error rate. The transmission diversity method rotates every single information symbol. The scheme can suppress channel noise and eliminate the interference term well. Simulation results show that the method can improve performance better than conventional algorithm without increasing decoding complexity.展开更多
LDPC codes are finding increasing use in applications requiring reliable and highly efficient information transfer over bandwidth. An LDPC code is defined by a sparse parity-check matrix and can be described by a bipa...LDPC codes are finding increasing use in applications requiring reliable and highly efficient information transfer over bandwidth. An LDPC code is defined by a sparse parity-check matrix and can be described by a bipartite graph called Tanner graph. Loops in Tanner graph prevent the sum-product algorithm from converging. Further, loops, especially short loops, degrade the performance of LDPC decoder, because they affect the independence of the extrinsic information exchanged in the iterative decoding. This paper, by graph theory, deduces cut-node tree graph of LDPC code, and depicts it with matrix. On the basis of tree matrix algorithm, whole depictions of loops can be figured out, providing of foundation for further research of relations between loops and LDPC codes’ performance.展开更多
With the integration of alternative energy and renewables,the issue of stability and resilience of the power network has received considerable attention.The basic necessity for fault diagnosis and isolation is fault i...With the integration of alternative energy and renewables,the issue of stability and resilience of the power network has received considerable attention.The basic necessity for fault diagnosis and isolation is fault identification and location.The conventional intelligent fault identification method needs supervision,manual labelling of characteristics,and requires large amounts of labelled data.To enhance the ability of intelligent methods and get rid of the dependence on a large amount of labelled data,a novel fault identification method based on deep reinforcement learning(DRL),which has not received enough attention in the field of fault identification,is investigated in this paper.The proposed method uses different faults as parameters of the model to expand the scope of fault identification.In addition,the DRL algorithm can intelligently modify the fault parameters according to the observations obtained from the power network environment,rather than requiring manual and mechanical tuning of parameters.The methodology was tested on the IEEE 14 bus for several scenarios and the performance of the proposed method was compared with that of population-based optimization methods and supervised learning methods.The obtained results have confirmed the feasibility and effectiveness of the proposed method.展开更多
文摘This paper proposes a scheme to construct orthogonal channel matrix for full rate quasiorthogonal STBC based on givens rotation with lower bit error rate. The transmission diversity method rotates every single information symbol. The scheme can suppress channel noise and eliminate the interference term well. Simulation results show that the method can improve performance better than conventional algorithm without increasing decoding complexity.
文摘LDPC codes are finding increasing use in applications requiring reliable and highly efficient information transfer over bandwidth. An LDPC code is defined by a sparse parity-check matrix and can be described by a bipartite graph called Tanner graph. Loops in Tanner graph prevent the sum-product algorithm from converging. Further, loops, especially short loops, degrade the performance of LDPC decoder, because they affect the independence of the extrinsic information exchanged in the iterative decoding. This paper, by graph theory, deduces cut-node tree graph of LDPC code, and depicts it with matrix. On the basis of tree matrix algorithm, whole depictions of loops can be figured out, providing of foundation for further research of relations between loops and LDPC codes’ performance.
基金supported by Fundamental Research Funds Program for the Central Universities(No.2019MS014)Key-Area Research and Development Program of Guangdong Province(No.2020B010166004).
文摘With the integration of alternative energy and renewables,the issue of stability and resilience of the power network has received considerable attention.The basic necessity for fault diagnosis and isolation is fault identification and location.The conventional intelligent fault identification method needs supervision,manual labelling of characteristics,and requires large amounts of labelled data.To enhance the ability of intelligent methods and get rid of the dependence on a large amount of labelled data,a novel fault identification method based on deep reinforcement learning(DRL),which has not received enough attention in the field of fault identification,is investigated in this paper.The proposed method uses different faults as parameters of the model to expand the scope of fault identification.In addition,the DRL algorithm can intelligently modify the fault parameters according to the observations obtained from the power network environment,rather than requiring manual and mechanical tuning of parameters.The methodology was tested on the IEEE 14 bus for several scenarios and the performance of the proposed method was compared with that of population-based optimization methods and supervised learning methods.The obtained results have confirmed the feasibility and effectiveness of the proposed method.
