Skip to main content
SpringerLink
Log in

Compressed Sensing for Channel State Information (CSI) Feedback in MIMO Broadcast Channels

  • Conference paper
Proceedings of the 4th International Conference on Computer Engineering and Networks

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 355))

  • 1251 Accesses

Abstract

In this paper, we proposed a new method to compress the CSI feedback. When the channel matrix is correlated, the DCT matrix works as a sparsifying basis to transform the channel matrix into a sparse form; the sparse signal is a feedback to the transmitter and reconstructed via the subspace pursuit (SP) recovery algorithm. Both theoretical analyses and simulation results show that the new method can introduce a huge computation cost reduction compared with the OMP algorithm and the codebook-based feedback scheme.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Baraniuk RG. Compressive sensing. IEEE Signal Process Mag. 2007;24(4):118–24.

    Article  Google Scholar 

  2. Candes E, Romberg J, Tao T. Stable signal recovery from incomplete and inaccurate measurements. Commun Pure Appl Math. 2006;59(8):1207–23.

    Article  MathSciNet  MATH  Google Scholar 

  3. Tosic I, Frossard P. Dictionary learning. IEEE Signal Process Mag. 2011;28(2):27–38.

    Article  Google Scholar 

  4. Candès EJ, Romberg J, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inform Theory. 2006;52(1):489–509.

    Article  MathSciNet  MATH  Google Scholar 

  5. Donoho DL. Compressed sensing. IEEE Trans Inform Theory. 2006;52(4):1289–306.

    Article  MathSciNet  MATH  Google Scholar 

  6. Luo P-H, Kung HT, Ting P-A. Compressive sensing based channel feedback protocols for spatially-correlated massive antenna arrays. In WCNC, 2012 IEEE; 2012. p. 492-497.

    Google Scholar 

  7. Jindal N. MIMO broadcast channels with finite-rate feedback. IEEE Trans Inform Theory. 2006;52:5045–60.

    Article  MathSciNet  MATH  Google Scholar 

  8. Dai W, Milenkovic O. Subspace pursuit for compressive sensing signal reconstruction. IEEE Trans Inform Theory. 2009;55(5):2230–49.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information and Electronics, Beijing Institute of Technology, 100081, Beijing, China

    Yuan Liu

  2. Beijing NARI Smartchip Microelectronics Technology Company Limited, 100081, Beijing, China

    Kuixi Chen

Authors
  1. Yuan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kuixi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuan Liu .

Editor information

Editors and Affiliations

  1. University of Texas at Dallas, Plano, Texas, USA

    W. Eric Wong

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Liu, Y., Chen, K. (2015). Compressed Sensing for Channel State Information (CSI) Feedback in MIMO Broadcast Channels. In: Wong, W. (eds) Proceedings of the 4th International Conference on Computer Engineering and Networks. Lecture Notes in Electrical Engineering, vol 355. Springer, Cham. https://doi.org/10.1007/978-3-319-11104-9_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-11104-9_18

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11103-2

  • Online ISBN: 978-3-319-11104-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation