| 引用本文: | 孙德春,李玉.一种改进的HOSVD降噪的信道预测算法[J].哈尔滨工业大学学报,2020,52(4):47.DOI:10.11918/201906147 |
| SUN Dechun,LI Yu.An improved channel prediction scheme for HOSVD denoising[J].Journal of Harbin Institute of Technology,2020,52(4):47.DOI:10.11918/201906147 |
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| 摘要: |
| 基于高阶奇异值分解(High Order Singular Value Decomposition, HOSVD)降噪的信道预测算法对天线数较少引起的秩不足问题比较敏感,同时也难以应付较大多普勒频移的情况,从而引起信道估计性能和预测性能的急剧下降、损失信道容量.针对这一问题,提出了一种改进的使用HOSVD降噪的信道预测算法.该算法先利用多输入多输出(Multiple-input Multiple-Output, MIMO)信道固有的空时相关性对采样得到的信道状态信息(Channel State Information, CSI)进行矩阵重排和数据平滑处理,随后基于信道的多维结构特性,使用HOSVD降低噪声的影响,继而重构信道矩阵,最后利用递归最小二乘滤波器对未来时刻的信道状态进行预测.仿真表明,所提算法的估计误差和预测误差性能均明显优于对比算法,这是因为所提算法通过矩阵重排和空时平滑,虚拟地增加了天线数,降低了秩缺失问题对估计和预测精度的影响,从而有效补偿了因误差所致的信道容量的损失.同时,对比天线数和多普勒频移对不同算法性能的影响可见,所提算法也能在大多普勒频移和天线数较少等不利条件下提供较好预测性能和信道容量,具有一定的优越性. |
| 关键词: 高阶奇异值分解降噪 信道预测 多输入多输出系统 递归最小二乘滤波器 平滑 |
| DOI：10.11918/201906147 |
| 分类号:TN911.7 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(61301170) |
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| An improved channel prediction scheme for HOSVD denoising |
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SUN Dechun,LI Yu
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(State Key Laboratory of Integrated Service Networks (Xidian University), Xi’an 710071, China)
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| Abstract: |
| Channel prediction algorithm based on high order singular value decomposition (HOSVD) denoising is sensitive to the small number of antennas and cannot cope with the maximum Doppler shift, which may result in a sharp drop in the channel estimation and prediction performance and the loss of channel capacity. To solve this problem, this paper proposes an improved channel prediction algorithm based on HOSVD denoising. The proposed algorithm first smoothes the sampled channel state information (CSI) by using the space-time correlation inherent in the multiple-input multiple-output channel, and then uses HOSVD to reduce the influence of noise and reconstruct the channel matrix based on the multi-dimensional structure characteristics of the channel. Finally, the recursive least squares filter is used to predict future CSI. Simulation results show that the channel estimation error and the prediction error of the proposed algorithm were better than those of the comparison algorithm. This is because the proposed algorithm uses matrix rearrangement and space-time smoothing to increase the number of antennas virtually, which can effectively reduce the influence of the Doppler shift factor and the rank missing problem caused by the small number of antennas on the prediction accuracy, thus compensating the loss of channel capacity. At the same time, by comparing the influence of antenna number and the Doppler frequency shift on the performance of different algorithms, it can be found that the proposed algorithm could provide better prediction performance and channel capacity under unfavorable conditions such as Doppler frequency shift and few antennas, which has certain advantages. |
| Key words: high order singular value decomposition denoising channel prediction multiple-input multiple-output system recursive least squares filter smoothing |
