BLIND CHANNEL AND SYMBOL JOINT ESTIMATION IN COOPERATIVE MIMO FOR WIRELESS SENSOR NETWORK  

作　　者：Yan Zhenya Zheng Baoyu 

机构地区：[1]Institute of Signal Processing ＆ Transmission, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

出　　处：《Journal of Electronics(China)》2008年第4期439-444,共6页电子科学学刊（英文版）

基　　金：the National Natural Science Foundation of China(No.60372107);the Ph.D.Innovation Programof Jiangsu Province(No.200670).

摘　　要：In this paper,application of Sequential Quasi Monte Carlo(SQMC)to blind channel andsymbol joint estimation in cooperative Multiple-Input Multiple-Output(MIMO)system is proposed,which does not need to transmit training symbol and can save the power and channel bandwidth.Additionally,an improved version of SQMC algorithm by taking advantage of current received signal isdiscussed.Simulation results show that the SQMC method outperforms the Sequential Monte Carlo(SMC)methods,and the incorporation of current received signal improves the performance of theSQMC obviously.In this paper,application of Sequential Quasi Monte Carlo（SQMC）to blind channel and symbol joint estimation in cooperative Multiple-Input Multiple-Output（MIMO）system is proposed,which does not need to transmit training symbol and can save the power and channel bandwidth.Additionally,an improved version of SQMC algorithm by taking advantage of current received signal is discussed.Simulation results show that the SQMC method outperforms the Sequential Monte Carlo （SMC）methods,and the incorporation of current received signal improves the performance of the SQMC obviously.

关 键 词：Cooperative Multiple-Input Multiple-Output （MIMO） Sensor network Sequential Quasi Monte Carlo （SQMC） 

分 类 号：TN92[电子电信—通信与信息系统]