针对在大规模多输入多输出(multiple-input multiple-output,MIMO)系统中,信道状态信息(channel state information,CSI)反馈量过大以及反馈的CSI过时的问题,提出一种基于自回归(autoregressive,AR)模型和主成分分析(principal componen...针对在大规模多输入多输出(multiple-input multiple-output,MIMO)系统中,信道状态信息(channel state information,CSI)反馈量过大以及反馈的CSI过时的问题,提出一种基于自回归(autoregressive,AR)模型和主成分分析(principal component analysis,PCA)方法的反馈算法。接收端进行信道估计获得CSI后,先利用AR模型预测出反馈所需时间之后的CSI;在此基础上计算压缩矩阵,然后利用PCA方法对预测的CSI进行压缩,再反馈给基站;最后基站端对接收到的CSI进行重构。从理论分析和仿真结果可以看出,该算法可以在降低反馈量的同时提高系统容量和信道恢复的准确性。展开更多
The estimation of sparse underwater acoustic channels with a large time delay spread is investigated under the framework of compressed sensing. For these types of channels, the excessively long impulse response will s...The estimation of sparse underwater acoustic channels with a large time delay spread is investigated under the framework of compressed sensing. For these types of channels, the excessively long impulse response will significantly degrade the convergence rate and tracking capability of the traditional estimation algorithms such as least squares （LS）, while excluding the use of the delay-Doppler spread function due to huge computational complexity. By constructing a Toeplitz matrix with a training sequence as the measurement matrix, the estimation problem of long sparse acoustic channels is formulated into a compressed sensing problem to facilitate the efficient exploitation of sparsity. Furthermore, unlike the traditional l1 norm or exponent-based approximation l0 norm sparse recovery strategy, a novel variant of approximate l0 norm called AL0 is proposed, minimization of which leads to the derivation of a hybrid approach by iteratively projecting the steepest descent solution to the feasible set. Numerical simulations as well as sea trial experiments are compared and analyzed to demonstrate the superior performance of the proposed algorithm.展开更多
In order to reduce the pilot number and improve spectral efficiency, recently emerged compressive sensing （CS） is applied to the digital broadcast channel estimation. According to the six channel profiles of the Eur...In order to reduce the pilot number and improve spectral efficiency, recently emerged compressive sensing （CS） is applied to the digital broadcast channel estimation. According to the six channel profiles of the European Telecommunication Standards Institute（ETSI） digital radio mondiale （DRM） standard, the subspace pursuit （SP） algorithm is employed for delay spread and attenuation estimation of each path in the case where the channel profile is identified and the multipath number is known. The stop condition for SP is that the sparsity of the estimation equals the multipath number. For the case where the multipath number is unknown, the orthogonal matching pursuit （OMP） algorithm is employed for channel estimation, while the stop condition is that the estimation achieves the noise variance. Simulation results show that with the same number of pilots, CS algorithms outperform the traditional cubic-spline-interpolation-based least squares （LS） channel estimation. SP is also demonstrated to be better than OMP when the multipath number is known as a priori.展开更多
基金The National Natural Science Foundation of China(No.11274259)the Open Project Program of the Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education(No.UASP1305)
文摘The estimation of sparse underwater acoustic channels with a large time delay spread is investigated under the framework of compressed sensing. For these types of channels, the excessively long impulse response will significantly degrade the convergence rate and tracking capability of the traditional estimation algorithms such as least squares （LS）, while excluding the use of the delay-Doppler spread function due to huge computational complexity. By constructing a Toeplitz matrix with a training sequence as the measurement matrix, the estimation problem of long sparse acoustic channels is formulated into a compressed sensing problem to facilitate the efficient exploitation of sparsity. Furthermore, unlike the traditional l1 norm or exponent-based approximation l0 norm sparse recovery strategy, a novel variant of approximate l0 norm called AL0 is proposed, minimization of which leads to the derivation of a hybrid approach by iteratively projecting the steepest descent solution to the feasible set. Numerical simulations as well as sea trial experiments are compared and analyzed to demonstrate the superior performance of the proposed algorithm.
基金The National Natural Science Foundation of China (No.60872075)the National High Technology Research and Development Program of China (863 Program) (No.2008AA01Z227)
文摘In order to reduce the pilot number and improve spectral efficiency, recently emerged compressive sensing （CS） is applied to the digital broadcast channel estimation. According to the six channel profiles of the European Telecommunication Standards Institute（ETSI） digital radio mondiale （DRM） standard, the subspace pursuit （SP） algorithm is employed for delay spread and attenuation estimation of each path in the case where the channel profile is identified and the multipath number is known. The stop condition for SP is that the sparsity of the estimation equals the multipath number. For the case where the multipath number is unknown, the orthogonal matching pursuit （OMP） algorithm is employed for channel estimation, while the stop condition is that the estimation achieves the noise variance. Simulation results show that with the same number of pilots, CS algorithms outperform the traditional cubic-spline-interpolation-based least squares （LS） channel estimation. SP is also demonstrated to be better than OMP when the multipath number is known as a priori.
