大规模MIMO-OFDM系统结构化压缩感知信道估计中导频优化方法研究

发布时间：2018-03-20 16:01

本文选题：大规模多输入多输出正交频分复用系统　切入点：结构化压缩感知　出处：《信号处理》2017年01期 　论文类型：期刊论文

【摘要】：对于FDD模式的大规模MIMO-OFDM系统,本文研究了将结构化压缩感知理论用于该系统的稀疏信道估计。考虑在该系统中将每个发送天线上的导频重叠放置,即每个发送天线可以在相同的时频资源块上发送导频符号,那么此时的系统稀疏信道估计问题可以建模为结构化压缩感知重建问题。为了优化导频位置和导频符号来改进稀疏信道估计的质量,提出了一种最小化完全块间相关值的导频优化准则以及基于此准则的导频搜索算法,完全块间相关值是结构化压缩感知框架下衡量恢复矩阵子块间相关程度的量值。仿真结果表明,与其他导频相比,使用此优化方法获得的导频可以使信道估计误差明显减小,其性能增益大约为2~5 d B。另外,仿真结果还表明,在相同的导频数量前提下,使用优化导频获得的性能增益会随着发送天线数量的增加而变得更加明显。
[Abstract]:For large scale MIMO-OFDM systems with FDD mode, this paper studies the application of structured compression sensing theory to sparse channel estimation of the system, and considers the placement of pilot overlaps on each transmit antenna in this system. That is, each transmit antenna can transmit pilot symbols on the same time-frequency resource block, Then the system sparse channel estimation problem can be modeled as structured compressed perceptual reconstruction problem. In order to optimize pilot position and pilot symbol to improve the quality of sparse channel estimation, A pilot optimization criterion for minimizing the correlation between complete blocks and a pilot search algorithm based on this criterion are proposed. The complete block correlation is a measure of the correlation between subblocks of the recovery matrix in the framework of structured compression perception. The simulation results show that compared with other pilot frequencies, the channel estimation error can be significantly reduced by using this optimization method. The performance gain is about 2 dB. In addition, the simulation results show that under the same pilot number, the performance gain obtained by using the optimized pilot becomes more obvious with the increase of the number of transmit antennas.
【作者单位】：南京邮电大学通信与信息工程学院;
【基金】：国家自然科学基金(61501248,61501254,61471202)
【分类号】：TN929.53

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