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60 GHz Ultra-Band Channel Estimation Based on Cluster-Classification Compressed Sensing

  • Xuebin Sun
  • Meng Hou
  • Hongbo Tao
  • Sha Zhang
  • Bin Li
  • Chengli Zhao
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 202)

Abstract

In this chapter, we investigated the application of compressed sensing in channel estimation for the emerging 60 GHz millimeter-wave communications. We firstly investigate the regular orthogonal matching pursuit (Regularized OMP) algorithm for 60 GHz systems, and then consider the characteristics of 60 GHz channel, a Cluster-based Classification Compressed Sensing Algorithm is finally proposed on this basis. It may significantly reduce the reconstruction error of the channel estimation. Error ratios of CS-ROMP and algorithm based Cluster-Classification are thoroughly compared and comprehensive analysis is given relying on the experimental simulations. The results show that CS-ROMP algorithms can be properly applied to channel estimation of the 60 GHz system. The developed Cluster-based Classification Compressed Sensing Algorithm shows a superior performance in both the precision of the channel estimation and the complexity of reconstruction.

Keywords

60GHz millimeter-wave Compressed sensing Channel estimations Cluster sparsity Cluster-sparsity compressive sensing 

Notes

Acknowledgments

Supported by the National Natural Science Foundation of China (Grant No.60902046, 60972079) and the Important National Science & Technology Specific Projects of China (Grant No.2011ZX03005-002, 2012ZX03001022)

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Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Xuebin Sun
    • 1
  • Meng Hou
    • 1
  • Hongbo Tao
    • 2
  • Sha Zhang
    • 2
  • Bin Li
    • 1
  • Chengli Zhao
    • 1
  1. 1.Key Laboratory of Universal Wireless CommunicationMinistry of Education, Beijing University of Posts and TelecommunicationsBeijingPeople’s Republic of China
  2. 2.The State Radio Monitoring Center Testing CenterBeijingPeople’s Republic of China

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