The performance loss of an approximately 3 dB signal-to-noise ratio is always paid with conventional differential detection compared to the related coherent detection. A new detection scheme consisting of two steps is...The performance loss of an approximately 3 dB signal-to-noise ratio is always paid with conventional differential detection compared to the related coherent detection. A new detection scheme consisting of two steps is proposed for the differential unitary space-time modulation （DUSTM） system. In the first step, the data sequence is estimated by conventional unitary space-time demodulation （DUSTD） and differentially encoded again to produce an initial estimate of the transmitted symbol stream. In the second step, the initial estimate of the symbol stream is utilized to initialize an expectation maximization （EM）-based iterative detector. In each iteration, the most recent detected symbol stream is employed to estimate the channel, which is then used to implement coherent sequence detection to refine the symbol stream. Simulation results show that the proposed detection scheme performs much better than the conventional DUSTD after several iterations.展开更多
时间复杂性是基于 EM 框架的贝叶斯网络学习算法应用的一个瓶颈问题.本文首先提出一种并行的参数EM 算法来学习具有缺省数据的贝叶斯网络参数,实验表明该算法可有效降低参数学习的时间复杂性.进而将该算法应用到结构 EM 算法中,提出一...时间复杂性是基于 EM 框架的贝叶斯网络学习算法应用的一个瓶颈问题.本文首先提出一种并行的参数EM 算法来学习具有缺省数据的贝叶斯网络参数,实验表明该算法可有效降低参数学习的时间复杂性.进而将该算法应用到结构 EM 算法中,提出一种并行的结构 EM 算法(PL-SEM),PL-SEM 算法并行地计算各个样本的期望充分因子和贝叶斯网络的参数,降低结构学习的时间复杂性.展开更多
基金The National Natural Science Foundation of China(No60572072,60496311)the National High Technology Research and Development Program of China (863Program) (No2006AA01Z264)+1 种基金the National Basic Research Program of China (973Program) (No2007CB310603)the PhD Programs Foundation of Ministry of Educa-tion of China (No20060286016)
文摘The performance loss of an approximately 3 dB signal-to-noise ratio is always paid with conventional differential detection compared to the related coherent detection. A new detection scheme consisting of two steps is proposed for the differential unitary space-time modulation （DUSTM） system. In the first step, the data sequence is estimated by conventional unitary space-time demodulation （DUSTD） and differentially encoded again to produce an initial estimate of the transmitted symbol stream. In the second step, the initial estimate of the symbol stream is utilized to initialize an expectation maximization （EM）-based iterative detector. In each iteration, the most recent detected symbol stream is employed to estimate the channel, which is then used to implement coherent sequence detection to refine the symbol stream. Simulation results show that the proposed detection scheme performs much better than the conventional DUSTD after several iterations.
文摘时间复杂性是基于 EM 框架的贝叶斯网络学习算法应用的一个瓶颈问题.本文首先提出一种并行的参数EM 算法来学习具有缺省数据的贝叶斯网络参数,实验表明该算法可有效降低参数学习的时间复杂性.进而将该算法应用到结构 EM 算法中,提出一种并行的结构 EM 算法(PL-SEM),PL-SEM 算法并行地计算各个样本的期望充分因子和贝叶斯网络的参数,降低结构学习的时间复杂性.
