Nano-volt magnetic resonance sounding(MRS) signals are sufficiently weak so that during the actual measurement, they are affected by environmental electromagnetic noise, leading to inaccuracy of the extracted characte...Nano-volt magnetic resonance sounding(MRS) signals are sufficiently weak so that during the actual measurement, they are affected by environmental electromagnetic noise, leading to inaccuracy of the extracted characteristic parameters and hindering effective inverse interpretation. Considering the complexity and non-homogeneous spatial distribution of environmental noise and based on the theory of adaptive noise cancellation, a model system for noise cancellation using multi-reference coils was constructed to receive MRS signals. The feasibility of this system with theoretical calculation and experiments was analyzed and a modified sigmoid variable step size least mean square(SVSLMS) algorithm for noise cancellation was presented. The simulation results show that, the multi-reference coil method performs better than the single one on both signal-to-noise ratio(SNR) improvement and signal waveform optimization after filtering, under the condition of different noise correlations in the reference coils and primary detecting coils and different SNRs. In particular, when the noise correlation is poor and the SNR<0, the SNR can be improved by more than 8 dB after filtering with multi-reference coils. And the average fitting errors for initial amplitude and relaxation time are within 5%. Compared with the normalized least mean square(NLMS) algorithm and multichannel Wiener filter and processing field test data, the effectiveness of the proposed method is verified.展开更多
信噪比是衡量信道质量的一个重要参数,该文主要研究LTE(Long Term Evolution)系统中基于探测参考信号(Sounding Reference Signal,SRS)的信噪比估计方法。针对DASS(Difference of Adjacent Subcarrier Signal)算法在高信噪比下噪声估计...信噪比是衡量信道质量的一个重要参数,该文主要研究LTE(Long Term Evolution)系统中基于探测参考信号(Sounding Reference Signal,SRS)的信噪比估计方法。针对DASS(Difference of Adjacent Subcarrier Signal)算法在高信噪比下噪声估计误差较大的这一缺点,该文提出一种适用于SRS的改进DASS方法。该方法通过重新定义子载波的差分方式,减小了噪声估计的误差,并且由于对连续的3个SRS频点,仅需要估计一次噪声,使得该文方法的复杂度仅为原DASS方法的1/3。仿真结果表明,所提方法的估计性能优于其余的方法,特别是在低时延和中等时延信道下,高信噪比时的估计精度提高了约10倍。展开更多
研究了大规模多输入多输出(Multiple⁃input Multiple⁃output,MIMO)正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统的信道预测方法,提出一种适用于实际噪声环境下的无线信道预测模型,该模型同时充分利用信道状态信...研究了大规模多输入多输出(Multiple⁃input Multiple⁃output,MIMO)正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统的信道预测方法,提出一种适用于实际噪声环境下的无线信道预测模型,该模型同时充分利用信道状态信息(Channel State Information,CSI)的时间与空间相关性。首先采用一种改进的卡尔曼滤波器修正上行探测导频信号估计的信道增益矩阵,所提出的改进的卡尔曼滤波器通过观测值修正上行探测导频信号估计的信道增益矩阵,使得估计值与信道矩阵真实值误差更小,同时该方法相较于传统的卡尔曼滤波器具有较低的计算复杂度。在此基础上,提出利用自回归模型(Auto Regressive,AR)在角度时延域进行信道预测。相较于其他域,例如天线频率域、天线时间域和角度频率域,角度时延域信道具有较为明显的信道稀疏性,有利于提高预测精度。最后,仿真结果验证了所提出的信道预测方法在实际噪声环境下优于传统AR信道预测方法。展开更多
基金Projects(41204079,41504086)supported by the National Natural Science Foundation of ChinaProject(20160101281JC)supported by the Natural Science Foundation of Jilin Province,ChinaProjects(2016M590258,2015T80301)supported by the Postdoctoral Science Foundation of China
文摘Nano-volt magnetic resonance sounding(MRS) signals are sufficiently weak so that during the actual measurement, they are affected by environmental electromagnetic noise, leading to inaccuracy of the extracted characteristic parameters and hindering effective inverse interpretation. Considering the complexity and non-homogeneous spatial distribution of environmental noise and based on the theory of adaptive noise cancellation, a model system for noise cancellation using multi-reference coils was constructed to receive MRS signals. The feasibility of this system with theoretical calculation and experiments was analyzed and a modified sigmoid variable step size least mean square(SVSLMS) algorithm for noise cancellation was presented. The simulation results show that, the multi-reference coil method performs better than the single one on both signal-to-noise ratio(SNR) improvement and signal waveform optimization after filtering, under the condition of different noise correlations in the reference coils and primary detecting coils and different SNRs. In particular, when the noise correlation is poor and the SNR<0, the SNR can be improved by more than 8 dB after filtering with multi-reference coils. And the average fitting errors for initial amplitude and relaxation time are within 5%. Compared with the normalized least mean square(NLMS) algorithm and multichannel Wiener filter and processing field test data, the effectiveness of the proposed method is verified.
文摘信噪比是衡量信道质量的一个重要参数,该文主要研究LTE(Long Term Evolution)系统中基于探测参考信号(Sounding Reference Signal,SRS)的信噪比估计方法。针对DASS(Difference of Adjacent Subcarrier Signal)算法在高信噪比下噪声估计误差较大的这一缺点,该文提出一种适用于SRS的改进DASS方法。该方法通过重新定义子载波的差分方式,减小了噪声估计的误差,并且由于对连续的3个SRS频点,仅需要估计一次噪声,使得该文方法的复杂度仅为原DASS方法的1/3。仿真结果表明,所提方法的估计性能优于其余的方法,特别是在低时延和中等时延信道下,高信噪比时的估计精度提高了约10倍。
文摘研究了大规模多输入多输出(Multiple⁃input Multiple⁃output,MIMO)正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)系统的信道预测方法,提出一种适用于实际噪声环境下的无线信道预测模型,该模型同时充分利用信道状态信息(Channel State Information,CSI)的时间与空间相关性。首先采用一种改进的卡尔曼滤波器修正上行探测导频信号估计的信道增益矩阵,所提出的改进的卡尔曼滤波器通过观测值修正上行探测导频信号估计的信道增益矩阵,使得估计值与信道矩阵真实值误差更小,同时该方法相较于传统的卡尔曼滤波器具有较低的计算复杂度。在此基础上,提出利用自回归模型(Auto Regressive,AR)在角度时延域进行信道预测。相较于其他域,例如天线频率域、天线时间域和角度频率域,角度时延域信道具有较为明显的信道稀疏性,有利于提高预测精度。最后,仿真结果验证了所提出的信道预测方法在实际噪声环境下优于传统AR信道预测方法。
