Skip to main content
SpringerLink
Log in

PAPR Reduction in OFDM Signals: An Adaptive-Network-Based Fuzzy Inference Approach

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In this paper, an adaptive-network-based fuzzy inference system (ANFIS) based scheme is analyzed and proposed for reducing the peak-to-average power ratio (PAPR) in multicarrier signals under additive white Gaussian noise and multipath fading (Raleigh) channel environment. This scheme involves training of ANFIS structure in time domain using Orthogonal Frequency Division Multiplexing signals with low PAPR, such as those obtained by approximate gradient project–null subcarrier switching (AGP–NCS) method. Once the ANFIS module is trained, the proposed scheme approximately offers similar reduction in PAPR as compared to AGP–NCS method, with significantly less convergence time and computational complexity. he results show that proposed approach is not only less complex but also maintains the data rate and bit error rate performance compared with other conventional schemes.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Litsyn, S. (2007). Peak power control in multicarrier communications. Cambridge: cambridge University Press.

    Book  Google Scholar 

  2. Md Mahmudul Hasan. (2014). A novel CVM precoding scheme for PAPR reduction in OFDM transmissions. Wireless Networks, 20(6), 1573–1581.

    Article  Google Scholar 

  3. Baig, I., Jeoti, V., Ikram, A. A., & Ayaz, Muhammad. (2014). PAPR reduction in mobile WiMAX: A novel DST precoding based random interleaved OFDMA uplink system. Wireless Networks, 20(5), 1213–1222.

    Article  Google Scholar 

  4. Jiang, T., & Yiyan, Wu. (2008). An overview: Peak-to-average power ratio reduction techniques for OFDM signals. IEEE Transactions on Broadcasting, 54(2), 257–268.

    Article  Google Scholar 

  5. 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 

  6. Cimini, L. J., & Sollenberger, N. R. (2000). Peak-to-average power ratio reduction of an OFDM signal using partial transmit sequences. IEEE Communication Letters, 4(3), 86–88.

    Article  Google Scholar 

  7. Lee, Byung Moo, Kim, Youngok, & de Figueiredo, Rui J. P. (2012). Performance analysis of the clipping scheme with SLM technique for PAPR reduction. Wireless Personal Communications, 63, 331–344.

    Article  Google Scholar 

  8. Krongold, B. S., & Jones, D. L. (2003). PAR reduction in OFDM via active constellation extension. IEEE Transaction on Broadcasting, 49(3), 258–268.

    Article  Google Scholar 

  9. Devlin, C. A., Zhu, A., & Brazil, T. J. (2008). Peak to average power ratio reduction technique for OFDM using pilot tones and unused carriers. In IEEE Radio and Wireless Symposium (pp. 33–36).

  10. Wong, K. T., Wang, B., & Chen, J.-C. (2011). OFDM PAPR reduction by switching null subcarriers and data subcarriers. IEEE Electronics Letters, 47(1), 62–63.

    Article  Google Scholar 

  11. Wang, Bo, Ho, Pin-Han, & Lin, Chih-Hao. (2012). OFDM PAPR reduction by shifting null subcarriers among data subcarriers. IEEE Communications Letters, 16(9), 62–63.

    Google Scholar 

  12. Mishra, A., Saxena, R., & Patidar, M. (2015). AGP–NCS scheme for PAPR reduction. Wireless Personal Communication. doi:10.1007/s11277-015-2275-8.

    Google Scholar 

  13. Mishra, A., Saxena, R., & Patidar, M. (2014). OFDM link with a better performance using artificial neural network. Wireless Personal Communication, 77, 1477–1487.

    Article  Google Scholar 

  14. Majumdar, K., & Das, Nabanita. (2005). Mobile user tracking using a hybrid neural network. Wireless Networks, 11(3), 275–284.

    Article  Google Scholar 

  15. Shen, X., Mark, Jon W., & Ye, Jun. (2000). User mobility profile prediction: An adaptive fuzzy inference approach. Wireless Networks, 6(5), 363–374.

    Article  MATH  Google Scholar 

  16. Emiroğlu, M., Beycioğlu, A., & Yildiz, S. (2012). ANFIS and statistical based approach to prediction the peak pressure load of concrete pipes including glass fiber. Expert Systems with Applications, 39(3), 2877–2883.

    Article  Google Scholar 

  17. Yesiloglu-Gultekin, N., Sezer, E. A., Gokceoglu, C., & Bayhan, H. (2013). An application of adaptive neuro fuzzy inference system for estimating the uniaxial compressive strength of certain granitic rocks from their mineral contents, Expert Systems with Applications. Expert Systems with Applications, 40(3), 921–928.

    Article  Google Scholar 

  18. Petković, Dalibor, Issa, Mirna, & Pavlović, Nenad D. (2013). Intelligent rotational direction control of passive robotic joint with embedded sensors. Expert Systems with Applications, 40(4), 1265–1273.

    Article  Google Scholar 

  19. Jang, J.-S. R. (1995). Adaptive-network-based fuzzy inference system. IEEE Transaction on Commununications, 43(6), 2111–2117.

    Article  Google Scholar 

  20. Takagi, H., & Sugeno, M. (1983). Derivation of fuzzy control rules from human operator’s control actions. In Proceedings of the IFAC symposium on fuzzy information, knowledge representation and decision analysis (pp. 55–60).

  21. Roger Jang, J.-S. (1991). Fuzzy modeling using generalized neural networks and Kalman filter algorithm. In Proceedings of the ninth National conference on artificial intelligence (AAAI-91) (pp. 762–767).

  22. Louliej, A., Jabrane, Y., Said, B. A. E., & Ouahman, A. A. (2013). Peak to average power ratio reduction in ECMA-368 ultra wideband communication systems using active constellation extension. Wireless Personal Communications, 70, 667–694.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Jaypee University of Engineering and Technology, Guna, MP, India

    Amit Mishra

  2. Department of Electrical Engineering, Jamia Millia Islamia (A Central University), New Delhi, India

    Zaheeruddin

Authors
  1. Amit Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zaheeruddin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amit Mishra.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mishra, A., Zaheeruddin PAPR Reduction in OFDM Signals: An Adaptive-Network-Based Fuzzy Inference Approach. Wireless Pers Commun 92, 587–601 (2017). https://doi.org/10.1007/s11277-016-3558-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-016-3558-4

Keywords

Search

Navigation