Computer Science ›› 2019, Vol. 46 ›› Issue (10): 103-108.doi: 10.11896/jsjkx.190700195

• Network & Communication • Previous Articles     Next Articles

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

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

CLC Number: 

  • TP391
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