Pub Date : 2013-01-01DOI: 10.3969/J.ISSN.1001-0548.2013.01.014
N. Wang, Guan Gui, Yongtao Su, Jingfeng Shi, Ping Zhang
Channel equalization and coherent detection require accurate channel state information(CSI) at the receiver for multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) systems.The conventional linear recovery methods,such as least squares(LS) and minimum mean square error(MMSE),are widely adapted in channel estimation under the assumption of rich multipath.However,numerous physical measurements have verified that the practical multipath channels tend to exhibit sparse structures.In this paper,exploiting the channel sparsity,we propose a compressive sensing-based CoSaMP recovery algorithm for MIMO-OFDM sparse channel estimation.Simulations show that the compressive sensing estimation method can obtain the accurate CSI with fewer pilots than conventional linear estimation for MIMO-OFDM systems at the cost of less computational complexity.The proposed method can greatly improve the spectrum efficiency for MIMO-OFDM systems.
{"title":"Compressive sensing-based sparse channel estimation method for MIMO-OFDM systems","authors":"N. Wang, Guan Gui, Yongtao Su, Jingfeng Shi, Ping Zhang","doi":"10.3969/J.ISSN.1001-0548.2013.01.014","DOIUrl":"https://doi.org/10.3969/J.ISSN.1001-0548.2013.01.014","url":null,"abstract":"Channel equalization and coherent detection require accurate channel state information(CSI) at the receiver for multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) systems.The conventional linear recovery methods,such as least squares(LS) and minimum mean square error(MMSE),are widely adapted in channel estimation under the assumption of rich multipath.However,numerous physical measurements have verified that the practical multipath channels tend to exhibit sparse structures.In this paper,exploiting the channel sparsity,we propose a compressive sensing-based CoSaMP recovery algorithm for MIMO-OFDM sparse channel estimation.Simulations show that the compressive sensing estimation method can obtain the accurate CSI with fewer pilots than conventional linear estimation for MIMO-OFDM systems at the cost of less computational complexity.The proposed method can greatly improve the spectrum efficiency for MIMO-OFDM systems.","PeriodicalId":35864,"journal":{"name":"电子科技大学学报","volume":"42 1","pages":"58-62"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70067639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-01-01DOI: 10.3969/J.ISSN.1001-0548.2013.05.018
Rongchang Zhao, K. Zhan, Xiao-jun Li, Yide Ma, Xiaowen Feng
An improved pulse coupled neural network(PCNN) model is proposed to solve combination optimization problem with help of PCNN auto-wave characteristic.Based on Tri-state cascading pulse coupled neural network(TCPCNN),a preventive feedback method by using the triangle inequality theorem is introduced.In the process of searching solutions,all solutions are judged by the triangle inequality theorem and solutions of poor quality are removed.Therefore,the solution space complexity of combinatorial optimization problems decreases and the efficiency and accuracy are improved.This algorithm is applied to the shor test path(SP) and the traveling salesman problem(TSP) simulations.The results show that the proposed algorithm can effectively reduce space complexity and further improve the searching speed.
{"title":"Preventive Feedback PCNN Model and Its Application in the Combinatorial Optimization Problems","authors":"Rongchang Zhao, K. Zhan, Xiao-jun Li, Yide Ma, Xiaowen Feng","doi":"10.3969/J.ISSN.1001-0548.2013.05.018","DOIUrl":"https://doi.org/10.3969/J.ISSN.1001-0548.2013.05.018","url":null,"abstract":"An improved pulse coupled neural network(PCNN) model is proposed to solve combination optimization problem with help of PCNN auto-wave characteristic.Based on Tri-state cascading pulse coupled neural network(TCPCNN),a preventive feedback method by using the triangle inequality theorem is introduced.In the process of searching solutions,all solutions are judged by the triangle inequality theorem and solutions of poor quality are removed.Therefore,the solution space complexity of combinatorial optimization problems decreases and the efficiency and accuracy are improved.This algorithm is applied to the shor test path(SP) and the traveling salesman problem(TSP) simulations.The results show that the proposed algorithm can effectively reduce space complexity and further improve the searching speed.","PeriodicalId":35864,"journal":{"name":"电子科技大学学报","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70067696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-01-01DOI: 10.3969/J.ISSN.1001-0548.2011.02.028
Xiao-hong Zhang, Feng Zhu
From the description of the pairs (low approximation,upper approximation) of rough sets,a new rough implication operator is introduced by modifying the method by Ref.[1],some algebraic properties of this rough implication operator are investigated,and these results are generalized to regular double Stone algebras and the following important result is proved: the regular double Stone algebra with the new rough implication operator is an MV-algebra.Further more a rough logic system RSL is constructed,its schematic is rough sets and extensional regular double Stone algebras.The completeness theorem of RSL is proved by introducing the notion of RSL-algebra.Finally,the relationship between rough logic RSL and fuzzy logic Luk(continuous-valued tukasiewicz logic system) is discussed.
{"title":"Rough logic system RSL and fuzzy logic system Luk","authors":"Xiao-hong Zhang, Feng Zhu","doi":"10.3969/J.ISSN.1001-0548.2011.02.028","DOIUrl":"https://doi.org/10.3969/J.ISSN.1001-0548.2011.02.028","url":null,"abstract":"From the description of the pairs (low approximation,upper approximation) of rough sets,a new rough implication operator is introduced by modifying the method by Ref.[1],some algebraic properties of this rough implication operator are investigated,and these results are generalized to regular double Stone algebras and the following important result is proved: the regular double Stone algebra with the new rough implication operator is an MV-algebra.Further more a rough logic system RSL is constructed,its schematic is rough sets and extensional regular double Stone algebras.The completeness theorem of RSL is proved by introducing the notion of RSL-algebra.Finally,the relationship between rough logic RSL and fuzzy logic Luk(continuous-valued tukasiewicz logic system) is discussed.","PeriodicalId":35864,"journal":{"name":"电子科技大学学报","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70067621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-01-01DOI: 10.3969/J.ISSN.1001-0548.2010.06.002
Xiaomei Lu, Wu‐Hua Chen, Xuanfang Yang
The problem of impulsive stabilization of delayed cellular neural networks (DCNNs) via partial states is discussed. The time delay is allowed to be time-varying. By utilizing the piecewise linear property of the activation function of DCNNs and applying piecewise differential Lyapunov combined with Razumikhin-type analysis techniques, a sufficient condition for the existence of the impulsive control law via partial states is derived. The sufficient condition is given in terms of linear matrix inequalities concerning the interconnection matrices and the bounds of the impulsive intervals. By using this result, an impulsive stabilization scheme for a class of DCNNs is proposed. The impulsive stabilization scheme only utilizes the output of partial states of the controlled DCNN. A numerical example illustrates the efficiency of the proposed method.
{"title":"Impulsive Stabilization of Delayed Cellular Neural Networks via Partial States","authors":"Xiaomei Lu, Wu‐Hua Chen, Xuanfang Yang","doi":"10.3969/J.ISSN.1001-0548.2010.06.002","DOIUrl":"https://doi.org/10.3969/J.ISSN.1001-0548.2010.06.002","url":null,"abstract":"The problem of impulsive stabilization of delayed cellular neural networks (DCNNs) via partial states is discussed. The time delay is allowed to be time-varying. By utilizing the piecewise linear property of the activation function of DCNNs and applying piecewise differential Lyapunov combined with Razumikhin-type analysis techniques, a sufficient condition for the existence of the impulsive control law via partial states is derived. The sufficient condition is given in terms of linear matrix inequalities concerning the interconnection matrices and the bounds of the impulsive intervals. By using this result, an impulsive stabilization scheme for a class of DCNNs is proposed. The impulsive stabilization scheme only utilizes the output of partial states of the controlled DCNN. A numerical example illustrates the efficiency of the proposed method.","PeriodicalId":35864,"journal":{"name":"电子科技大学学报","volume":"39 1","pages":"810-816"},"PeriodicalIF":0.0,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70067570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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