计算机科学 ›› 2019, Vol. 46 ›› Issue (10): 103-108.doi: 10.11896/jsjkx.190700195

• 网络与通信 • 上一篇    下一篇

欠定条件下基于主成分的亚采样信号重构

王鹏飞, 张杭   

  1. (中国人民解放军陆军工程大学通信工程学院 南京210007)
  • 收稿日期:2019-07-29 修回日期:2019-09-27 出版日期:2019-10-15 发布日期:2019-10-21
  • 通讯作者: 张杭(1962-),女,教授,博士生导师,主要研究方向为通信信号处理、认知无线电、卫星通信,E-mail:hangzh_2002@163.com。
  • 作者简介:王鹏飞(1986-),男,博士生,高级工程师,主要研究方向为通信信号处理和移动卫星通信,E-mail:wangpengfei_2004@163.com。
  • 基金资助:
    本文受国家自然科学基金(61671475)资助。

Sub-sampling Signal Reconstruction Based on Principal Component Under Underdetermined Conditions

WANG Peng-fei, ZHANG Hang   

  1. (College of Communications Engineering,Army Engineering University of PLA,Nanjing 210007,China)
  • Received:2019-07-29 Revised:2019-09-27 Online:2019-10-15 Published:2019-10-21

摘要: 传统的信息采集还原方式的资源消耗高,对信息数据的利用效率和处理效率较低,难以适应瞬息万变的战场信息感知环境,而且复杂的电磁对抗环境会造成测量通道维度的动态变化,进一步加剧了信息采集还原的难度。在大规模多输入多输出无线通信系统场景下,利用信息数据在变换域空间中的稀疏特性,提出了一种基于压缩感知理论的亚采样重构方案。该方案利用主成分基变换的方式实现信息数据的稀疏化,采用子空间追踪的方式实现信号的亚采样还原,对测量通道维度的动态变化具有较强的鲁棒性。同时,采用分块思想避免了高阶矩阵参与处理过程中的迭代运算,使得算法具有更好的求解精度和效率,实现了欠定条件下信息数据的高效重构。

关键词: 大规模多输入多输出, 信号重构, 压缩感知, 亚采样, 主成分分析

Abstract: The traditional sampling and reduction method has low utilization and processing efficiency of information data with high consumption of resource,so it cannot adapt to the perception of battlefield information which is constantly changing.Under the complicated electromagnetism circumstance,dynamic changes of the measuring dimensions increase the difficulty of signal acquisition and reduction.In the case of massive multi-input and multi-output wireless communication systems,this paper proposed a sub-sampling reconstruction scheme based on the theory of compressed sensing by using the sparse characteristics of signal data in transform domain space.This scheme uses principal component basis transformation to achieve sparse structure of the signal matrices,and completes the restoration of signal data with sub-sampling by using subspace pursuit.The proposed algorithm is robust to the dynamic changes of the measuring dimension,and it also avoids the high-order matrices participating in the iterative operation process,which can make the algorithm have better accuracy and efficiency of solution by blocking the signal matrices.Finally,the efficient reconstruction of information data under underdetermined conditions is achieved.

Key words: Compressed sensing, Massive MIMO, Principal component analysis, Signal reconstruction, Sub-sampling

中图分类号: 

  • TP391
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