吉林大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (3): 985-990.doi: 10.13229/j.cnki.jdxbgxb201503044

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Compressed sensing reconstruction of LDPC code

ZHANG Yi, DA Xin-yu, CHU Zhen-yong   

  1. School of Information and Navigation, Air Force Engineering University, Xi'an 710077, China
  • Received:2013-08-23 Online:2015-05-01 Published:2015-05-01

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Abstract: In order to resolve the problem of signal reconstruction error caused by observation noise in Compressed Sensing (CS), a method is proposed to reconstruct error vectors using the theory of non-correlation constraint as the condition. In this method, a linear programming for error vectors is established based on the annihilator property of sparse check matrix; and compressed sensing reconstruction for Low-Density Parity-Check (LDPC) codes is implemented. Under an additive white Gauss noise channel and primal-dual interior point algorithm, simulations show that three selected (LDPC) codes all satisfy non-correlation constraint condition with high reconstruction ability. Moreover, the correlation coefficient of MacKay random codes is smaller, therefore 100% success rate is obtained in reconstructing error vectors with SNR of -1 dB. The simulation results also show that under the condition of satisfying the requirement of Bit Error Rate (BER) performance, high reliability communication can be achieved with low SNR by the proposed CS-LDPC codes.

Key words: communication, compressed sensing, low-density parity-check codes, signal reconstruction, cross-correlation coefficients, annihilator matrix

CLC Number: 

  • TN911.2
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