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7篇 您的检索式:作者名="MAO JunLing"
    题名 作者 年代 出处 被引量
1Robust multiuser MIMO scheduling algorithms with imperfect CSI显示文摘Robust multiuser multiple-input-multiple-output (MIMO) scheduling algorithms are proposed in this paper.With imperfect channel state information (CSI),traditional scheduling algorithms for the multiuser MIMO system based on the zero forcing precoding scheme will lose some performance due to the multi-user interference (MUI).In order to improve the system average throughput,we study the robust multiuser MIMO scheduling problem with imperfect CSI.From the average capacity formula,we derive a robust factor which can transform the robust multiuser MIMO scheduling problem into the traditional one,thus most existing non-robust scheduling algorithms can be robust if this factor is adopted.Simulation results show that compared with the traditional algorithms,the proposed robust algorithms can improve the system average throughput significantly under the CSI error environment.MAO JunLing GAO JinChun LIU YuanAn XIE Gang ZHANG Jian 2012Science China(Information Sciences)2012,55,4:4
2Energy Efficiency Optimization for OFDM-Based Cognitive Radio Systems: A Water-Filling Factor Aided Search Method 显示文摘MAO Junling XIE Gang GAO Jinchun 2013IEEE Transactions on Wireless Communications2013,12,5:1
3Effect of Maotai liquor on P53 gene in primary hepatocellular carcinoma induced by compound factors in rats显示文摘In China,the morbidity of alcoholic liver diseases (ALD)is increasing,which is the second cause of liver diseases after viral hepatitis.An epidemiological survey of 99 subjects in Kweichow Maotai Distillery who have drunk a lot of Maotai liquor for a long time showed that fatty livers were predominate and the incidence of hepatic fibrosis and cirrhosis was relatively low.1 Now,we carried out an animal experiment to investigate the mutation of P53 gene in liver tissues of primary hepatocellular carcinoma (HCC) induced by compound factors.Zhao Xueke Cheng Mingliang Mu Mao Liu Junling He Chang Zhang Quan 2014Chinese Medical Journal2014,,2:1
4A New Species of Cupressinocladus from the Lower Cretaceous of Guyang Basin,Inner Mongolia,China and Cluster Analysis显示文摘Cupressinocladus Seward is a fossil genus of conifers and conifer fossils with reproductive organs are very rare.In general,it is difficult to understand the natural affinities with other conifers.In this paper,a new species,Cupressinocladus guyangensis P.H.Jin et B.N.Sun sp.nov.,is reported based on branches with immature female cones from the Lower Cretaceous Guyang Formation of the Guyang Basin in Inner Mongolia,northern China.The foliage shoots are decussate.Leaves are decussate,imbricate,scale-like,weakly dimorphic,and bear longitudinal glands on the abaxial view.Stomata complexes are haplocheilic,monocyclic,irregularly arranged,and spread along the leaf margin.Immature female cones are subglobose with 6-8 cone scales,and three subglobose ovules arranged in a row at the base of the cone scales.Moreover,we performed cluster analysis using a statistics and machine learning toolbox for 23 fossils and extant species based on 16 morphological characters.The result implies that the new species bears a close resemblance to the extant Cupressus funebris Endl.and might have nearest systematic affinities to it.JIN Peihong MAO Teng DONG Junling WANG Zixi SUN Mingxuan XU Xiaohui DU Baoxia SUN Bainian 2017Acta Geologica Sinica(English Edition)2017,91,4:1
5Energy efficiency optimization for OFDM-based cognitive radio systems: a water-filling factor aided search method显示文摘MAO Junling XIE Gang GAO Jinchun 2013IEEE Trans on Wireless Communications2013,12,5:1
6Resource allocation for MIMO-OFDMA downlink based cognitive radio systems with imperfect channel learning显示文摘This paper is concerned with the resource allocation for multiple input multiple output and orthogonal frequency division multiplexing access(MIMO-OFDMA)downlink cognitive radio systems where a cognitive radio MIMO-OFDMA system is under spectrum sharing with an existing primary radio(PR)network.We use the channel learning scheme to estimate the channel information from cognitive radio transmitter(CR-TX)to PR and then make beamforming to transmit signal.Considering the interference from CR-TX to PR caused by the imperfect channel learning,we intend to maximize CR throughput under the interference power constraint at PR and CR transmit power constraint.A nearly optimal subcarrier and power allocation algorithm with linear complexity is proposed.The proposed algorithm is global optimal when the maximum transmit power is beyond certain threshold.Simulation results show that the proposed algorithm has a good performance very close to the global optimal algorithm.LI XiuWen GAO JinChun LIU YuanAn XIE Gang MAO JunLing DENG PanLiang 2013Science China(Information Sciences)2013,56,10:0
7Decentralized transceiver optimization for multi-relay aided multiuser MIMO networks显示文摘In this paper,the decentralized transceivers and corresponding channel state information(CSI)signaling concepts are proposed for weighted sum rate(WSR)maximization,via linear downlink transceiver optimization in multi-relay aided multiuser multiple-input multiple-output(MIMO)networks.Three decentralized algorithms are proposed with the local CSI requirements at the base station(BS)and the relay nodes.In the first proposed algorithm,the generalized channel inversion precoding is used at the BS to avoid the second hop channel sharing requirements among the relay nodes,and decentralized transceiver optimization of the relay nodes and users are developed to maximize the WSR.A minimum mean square error(MMSE)-based enhanced method is proposed as well.To further improve the performance,we provide a novel structure of the precoding matrix of the BS.The decentralized joint BS,relay and user transceiver optimization problem is formulated and solved.Also,the CSI signaling and decentralized processing in the time division duplex(TDD)system are analyzed.Simulation results show the effectiveness of the decentralized transceiver design compared to the existing algorithms.SUN Qi LI LiHua MAO JunLing 2014Science China(Information Sciences)2014,57,8:0
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