Skip to main content
SpringerLink
Log in

A Low-Complexity Compressive Sensing Algorithm for PAPR Reduction

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

One of the major challenges in orthogonal frequency division multiplexing system is its high peak-to-average-power ratio (PAPR). Among the existing PAPR reduction technologies, clipping is the most often used one due to its simplicity of implementation. But it induces signal distortion. In this paper, we propose a new PAPR reduction method which introduces compressive sensing theory to help the clipping and signal recovery processes. Our method has superior symbol-error-rate (SER) performance compared with traditional clipping, and at the same time has better PAPR reduction performance compared with traditional tone reservation based algorithms. What is more, different from the existing high-complexity compressive sensing based scheme which tries to solve an optimization problem, the proposed algorithm uses orthogonal matching pursuit scheme to recover the distorted signals, thereby it significantly reduces the computational complexity with the same PAPR reduction efficiency. Simulation results show that our proposed scheme can achieve dramatic PAPR reduction with only about \(10\,\%\) of the existing method, while still keeps good SER performance.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Weinstein, S. B., & Ebert, P. M. (1971). Data transmission by frequency division multiplexing using the discrete Fourier transform. IEEE Transactions on Communications, 19, 628–634.

    Article  Google Scholar 

  2. Cimini, L. J, Jr. (1985). Analysis and simulation of a digital mobile chaneel using orthogonal frequency division multiplexing. IEEE Transactions on Communications, 33, 665–675.

    Article  Google Scholar 

  3. Han, S. H., & Lee, J. H. (2005). An overview of peak-to-average power ratio reduction techniques for multicarrier transmission. IEEE Wireless Communications, 12(2), 56–65.

    Article  Google Scholar 

  4. Bae, K., Andrews, J. G., & Powers, E. J. (2010). Adaptive active constellation extension algorithm for peak-to-average ratio reduction in OFDM. IEEE Communications Letters, 14(1), 39–41.

    Article  Google Scholar 

  5. Tellado, J. (2000). Multicarrier modulation with low PAR—applicaiton to DSL and wireless. Dordrecht: Kulwer Academic Publication.

    Google Scholar 

  6. Mahafeno, I. M., Louet, Y., & Helard, J.-F. (2009). Peak-to-average power ration reduction using second order cone programming based tine reservation for terrestrial digital video broadcasting systems. IET Communications, 3(7), 1250–1261.

    Article  Google Scholar 

  7. Krongold, B. S., & Jones, D. L. (2004). An active-set approach for OFDM PAR reduction via tone reservation. IEEE Transactions on Signal Processing, 52(2), 495–509.

    Article  MathSciNet  Google Scholar 

  8. Wang, M., Quevedo, D. E., Goodwins, G. C., & Krongold, B. S. (2008). A complex-baseband active-set approach for tone reservation PAR reduction in OFDM systems (pp. 113–117). AusCTW: Communications Theory Workshop.

  9. Liang, Q., Wen, Q., Xiao, Y., & Li, S. (2009). A comparison of SCR and active-set methods for PAPR reduction in OFDM systems. In Proceedings of the international conference on networks, Security (pp. 489–495).

  10. Jung-Chieh, C., & Chih-Peng, L. (2010). Tone reservation using near-optimal peak reduction tone set selection algorithm for PAPR reduction in OFDM systems. IEEE signal Processing Letters, 17(11), 933–936.

    Article  Google Scholar 

  11. Baraniuk, R. G. (2007). Compressive sensing. IEEE Signal Processing Magazine, 24, 118–124.

    Article  Google Scholar 

  12. Tropp, J., & Gilbert, A. (2007). Signal recovery from random measurements via orthogonal matching pursuit. IEEE Transactions on Information Theory, 53, 4655–4666.

    Article  MATH  MathSciNet  Google Scholar 

  13. Chen, S., Donoho, D., & Saunders, M. (1998). Atomic decomposition by basis pursuit. SIAM Journal on Scientific Computing, 20(1), 33–61.

    Article  MathSciNet  Google Scholar 

  14. Donoho, D., & Tanner, J. (2005). Neighborliness of randomly projected simplicies in high dimensions. Preprint.

  15. Al-Safadi, E. B., & Al-Naffouri, T. Y. (2009). On resducing the complexity of tone reservation based PAPR reduction schemes by compressive sensing. In Proceedings of the IEEE Globecom 2009. Honolulu HI.

  16. Grant, M., Boyd, S. (2009). CVX: Matlab software for disciplined convex programming (web page and software). http://stanford.edu/boyd/cvx. Feb 2009.

Download references

Author information

Authors and Affiliations

  1. Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China

    Bo Liu, Si Liu & Lin Gui

  2. Key Laboratory of Wireless Sensor Network and Communication, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, 865 Changning Road, Shanghai , 200050, China

    Yingguan Wang

  3. Shanghai Advanced Research Institute, Chinese Academy of Sciences, and National Mobile Communications Research Laboratory, Southeast University, Nanjing , China

    Yun Rui

Authors
  1. Bo Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Si Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yun Rui
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lin Gui
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yingguan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bo Liu.

Additional information

This work was supported by National Natural Science Foundation of China (61221001, 61201222, 61302093, 61362013), the 111 Project (B07022) and the Shanghai Key Laboratory of Digital Media Processing and Transmissions. It is also partly supported by the open research fund of National Mobile Communications Research Laboratory, Southeast University (2013D07)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, B., Liu, S., Rui, Y. et al. A Low-Complexity Compressive Sensing Algorithm for PAPR Reduction. Wireless Pers Commun 78, 283–295 (2014). https://doi.org/10.1007/s11277-014-1753-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-014-1753-8

Keywords

Search

Navigation