Skip to main content
SpringerLink
Log in

PAPR Reduction of GFDM Signals Using Encoder-Decoder Neural Network (Autoencoder)

  • SHORT COMMUNICATION
  • Published:
National Academy Science Letters Aims and scope Submit manuscript

Abstract

These days, one of the major downsides of Generalized Frequency Division Multiplexing (GFDM) systems is a high peak-to-average power ratio (PAPR). In this research, we present a novel deep learning autoencoder-based method to lower the PAPR of GFDM. The PAPR-reducing network (PRNet), also known as the PAPR-reducing method, is based on the encoder-decoder neural network (Autoencoder). In the PAPR-reducing network (PRNet), the bit error rate (BER) and the PAPR of the GFDM system are jointly minimised by adaptively determining the constellation mapping and damping of symbols on each subcarrier and sub-symbol.

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

References

  1. Liu K, Deng W, Liu Y (2019) Theoretical analysis of the peak-to-average power ratio and optimal pulse shaping filter design for GFDM systems. IEEE Trans Signal Process 67(13):3455–3470

    Article  MathSciNet  MATH  ADS  Google Scholar 

  2. Zhang X, Wang Z, Ning X, Xie H (2020) On the performance of GFDM assisted NOMA schemes. IEEE Access 8:88961–88968

    Article  Google Scholar 

  3. Çağlayan SF, İsbit M, İlgün S, Erdoğan E (2020) Design and comparison of GFDM receivers for 5G communication systems. In: 2020 28th signal processing and communications applications conference (SIU), Gaziantep, Turkey, pp 1–4, doi : https://doi.org/10.1109/SIU49456.2020.9302286

  4. Michailow N et al (2014) Generalized frequency division multiplexing for 5th generation cellular networks. IEEE Trans Commun 62(9):3045–3061

    Article  Google Scholar 

  5. Fettweis G, Krondorf M, Bittner S (2009) “GFDM - generalized frequency division multiplexing. In: Vehicular Technology Conference 2009 VTC Spring. IEEE 69th, Barcelona, pp 1–4

  6. Ihalainen T, Ikhlef A, Louveaux J, Renfors M (2011) Channel equalization for multi-antenna FBMC/OQAM receivers. IEEE Trans Veh Tech 60(5):2070–2085

    Article  Google Scholar 

  7. Z. Sharifian MJ, Omidi A, Farhang H, (2015) Saeedi- Sourck, “Polynomial-based compressing and iterative expanding for PAPR reduction in GFDM”, In: 2015 23rd Iranian conference on electrical engineering, pp 518–523

  8. Sharifian Z, Omidi MJ, Saeedi-Sourck H, Farhang A (2016) Linear precoding for PAPR reduction of GFDMA. IEEE Wireless Commun Lett 5(5):520–523. https://doi.org/10.1109/LWC.2016.2597837

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Electrical and Computer Engineering, Kennesaw State University, Kennesaw, USA

    Sumit Chakravarty

  2. Department of Electronics and Communication Engineering, New Horizon College of Engineering, Bengaluru, India

    Arun Kumar

Authors
  1. Sumit Chakravarty
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arun Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arun Kumar.

Ethics declarations

Conflict of interest

All authors do not have any conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chakravarty, S., Kumar, A. PAPR Reduction of GFDM Signals Using Encoder-Decoder Neural Network (Autoencoder). Natl. Acad. Sci. Lett. 46, 213–217 (2023). https://doi.org/10.1007/s40009-023-01230-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40009-023-01230-1

Keywords

Search

Navigation