Cluster Computing

, Volume 22, Supplement 2, pp 2971–2980 | Cite as

Deterministic compressed sensing based channel estimation for MIMO OFDM systems

  • Kai WangEmail author
  • Zhichun Gan
  • Jingzhi Liu
  • Wei He
  • Shun Xu


In most of the existing compressed sensing (CS) based channel estimation schemes for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, the randomly allocated pilot is difficult to be implemented in real applications and introduces additional pilot overhead for transmitting the information on pilot locations which is required in channel reconstruction at receiver. In this paper, a channel estimation scheme based on deterministic compressed sensing is proposed to cut down the pilot overhead in MIMO OFDM systems. To be specific, a deterministic pilot placement scheme is proposed to select the subset of the subcarriers for pilot transmission. Since this deterministic pilot placement leads a new kind of deterministic measurement matrices in CS model, the mutual coherence property of the deterministic matrix is verified to establish theoretical guarantee for the pilot placement scheme. Then an improved reconstruction algorithm is proposed to match the structure of the deterministic matrix. Numerical results demonstrate that even without the pilot locations information, the proposed channel estimation scheme based on deterministic compressed sensing achieves similar estimation accuracy as conventional estimator with random pilot placement.


MIMO OFDM Channel estimation Compressed sensing Deterministic Mutual coherence 



This research is funded by the Program for New Century Excellent Talents in University (No. NCET -11 - 0873), the Program for Innovative Research Team in University of Chongqing (No. KJTD 201343), Program for Fundamental Research of Chongqing Communication Institute (No. TZ –CQTY–Y–C–2016-023) and the open subject of the Chongqing key laboratory of emergency communication (No. IRT1299).


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Kai Wang
    • 1
    • 2
    Email author
  • Zhichun Gan
    • 2
  • Jingzhi Liu
    • 1
  • Wei He
    • 1
  • Shun Xu
    • 1
  1. 1.Communication Sergeant InstitutePLA Army Engineering UniversityChongqingChina
  2. 2.Information Communication InstituteNational University of Defence TechnologyWuhanChina

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