This paper investigates joint design and optimization of both low density parity check （LDPC） codes and M-algorithm based detectors including iterative tree search （ITS） and soft-output M-algorithm （SOMA） in mul...This paper investigates joint design and optimization of both low density parity check （LDPC） codes and M-algorithm based detectors including iterative tree search （ITS） and soft-output M-algorithm （SOMA） in multiple-input multiple-output （MIMO） systems via the tool of extrinsic information transfer （EXIT） charts. First, we present EXIT analysis for ITS and SOMA. We indicate that the extrinsic information transfer curves of ITS obtained by Monte Carlo simulations based on output log-likelihood rations are not true EXIT curves, and the explanation for such a phenomenon is given, while for SOMA, the true EXIT curves can be computed, enabling the code design. Then, we propose a new design rule and method for LDPC code degree profile optimization in MIMO systems. The algorithm can make the EXIT curves of the inner decoder and outer decoder match each other properly, and can easily attain the desired code with the target rate. Also, it can transform the optimization problem into a linear one, which is computationally simple. The significance of the proposed optimization approach is validated by the simulation results that the optimized codes perform much better than standard non-optimized ones when used together with SOMA detector.展开更多
轨道交通LTE-M(Long Term Evolution-Metro,基于轨道交通的长期演进)同频干扰检测关乎列控信号传输的可靠性,提出一种基于INFO(weIghted meaNoFvectOrs,基于向量加权平均)算法的盲源分离方法,即INFO-BSS。该方法以混合信号的最大化负熵...轨道交通LTE-M(Long Term Evolution-Metro,基于轨道交通的长期演进)同频干扰检测关乎列控信号传输的可靠性,提出一种基于INFO(weIghted meaNoFvectOrs,基于向量加权平均)算法的盲源分离方法,即INFO-BSS。该方法以混合信号的最大化负熵为目标函数,用INFO优化算法替代牛顿迭代法,解决了牛顿迭代法初始参数易设置不当以及容易陷入局部最优的问题。仿真结果对比表明,在不同分辨率带宽、不同信干比等条件下,INFO-BSS的检测性能都要优于常规算法。展开更多
铂电阻传感器的温度测量系统为动态测量系统,其参数处于时变状态,传统不确定度评定方法的静态假设并不适用。因此,本文提出了一种基于马尔科夫蒙特卡洛(MCMC)的贝叶斯统计的铂电阻传感器动态不确定度评定方法,构建了云边端协同的在线计...铂电阻传感器的温度测量系统为动态测量系统,其参数处于时变状态,传统不确定度评定方法的静态假设并不适用。因此,本文提出了一种基于马尔科夫蒙特卡洛(MCMC)的贝叶斯统计的铂电阻传感器动态不确定度评定方法,构建了云边端协同的在线计量系统对动态测量系统进行实时监测;基于贝叶斯信息融合原理实现动态测量系统信息的动态更新;基于Metropolis-Hastings(M-H)算法求解贝叶斯信息融合后验分布的唯一收敛马氏链,有效减少了参数间的相关性,实现了动态测量系统的不确定度动态评定。实验对比GUM法(Guide to the Uncertainty in Measurement)、传统贝叶斯法和改进贝叶斯法的不确定度评定结果,结果表明,本文方法可有效提高温度测量动态系统的不确定度评定结果的精度,解决了铂电阻温度测量动态系统量值可靠性无法估计的问题。展开更多
基金Supported by the National Basic Research Program of China (Grant No. 2009CB320406)the National Natural Science Foundation of China(Grant No. 60872048)Specialized Major Science and Technology Project of China (Grant Nos. 2008ZX03003-004, 2009ZX03003-009)
文摘This paper investigates joint design and optimization of both low density parity check （LDPC） codes and M-algorithm based detectors including iterative tree search （ITS） and soft-output M-algorithm （SOMA） in multiple-input multiple-output （MIMO） systems via the tool of extrinsic information transfer （EXIT） charts. First, we present EXIT analysis for ITS and SOMA. We indicate that the extrinsic information transfer curves of ITS obtained by Monte Carlo simulations based on output log-likelihood rations are not true EXIT curves, and the explanation for such a phenomenon is given, while for SOMA, the true EXIT curves can be computed, enabling the code design. Then, we propose a new design rule and method for LDPC code degree profile optimization in MIMO systems. The algorithm can make the EXIT curves of the inner decoder and outer decoder match each other properly, and can easily attain the desired code with the target rate. Also, it can transform the optimization problem into a linear one, which is computationally simple. The significance of the proposed optimization approach is validated by the simulation results that the optimized codes perform much better than standard non-optimized ones when used together with SOMA detector.
文摘轨道交通LTE-M(Long Term Evolution-Metro,基于轨道交通的长期演进)同频干扰检测关乎列控信号传输的可靠性,提出一种基于INFO(weIghted meaNoFvectOrs,基于向量加权平均)算法的盲源分离方法,即INFO-BSS。该方法以混合信号的最大化负熵为目标函数,用INFO优化算法替代牛顿迭代法,解决了牛顿迭代法初始参数易设置不当以及容易陷入局部最优的问题。仿真结果对比表明,在不同分辨率带宽、不同信干比等条件下,INFO-BSS的检测性能都要优于常规算法。
文摘铂电阻传感器的温度测量系统为动态测量系统,其参数处于时变状态,传统不确定度评定方法的静态假设并不适用。因此,本文提出了一种基于马尔科夫蒙特卡洛(MCMC)的贝叶斯统计的铂电阻传感器动态不确定度评定方法,构建了云边端协同的在线计量系统对动态测量系统进行实时监测;基于贝叶斯信息融合原理实现动态测量系统信息的动态更新;基于Metropolis-Hastings(M-H)算法求解贝叶斯信息融合后验分布的唯一收敛马氏链,有效减少了参数间的相关性,实现了动态测量系统的不确定度动态评定。实验对比GUM法(Guide to the Uncertainty in Measurement)、传统贝叶斯法和改进贝叶斯法的不确定度评定结果,结果表明,本文方法可有效提高温度测量动态系统的不确定度评定结果的精度,解决了铂电阻温度测量动态系统量值可靠性无法估计的问题。
