Deep neural networks (DNN) have gained considerable attention in the communication literature during the past few years. In particular, as a well-known DNN architecture, autoencoders (AE) are used to model the end-to-end communication systems achieving a reasonable performance in terms of block error rate (BLER). However, autoencoders significantly suffer from high peak-to-average-power-ratio (PAPR), resulting in power amplifier saturation. This paper proposes a novel DNN architecture for reducing PAPR in autoencoder-based communication systems. Simulation results verify that the proposed scheme outperforms the conventional PAPR reduction method, i.e., loss function-based PAPR reduction approach, in terms of both bit error rate (BER) and PAPR.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    PAPR Reduction Scheme for Deep Learning-Based Communication Systems Using Autoencoders


    Contributors:


    Publication date :

    2020-05-01


    Size :

    229580 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Deep Tracking Portfolios Using Autoencoders and Variational Autoencoders

    Urrego, Daniel Aragón / Nieto, Oscar Eduardo Reyes / Quimbayo, Carlos Andrés Zapata | Springer Verlag | 2024


    Pilot-Assisted PAPR Reduction Technique for Optical OFDM Communication Systems

    Popoola, W.O. / Ghassemlooy, Z. / Stewart, B.G. | British Library Online Contents | 2014


    Analysis of driving skills based on deep learning using stacked autoencoders

    Kagawa, Takuya / Chandrasiri, Naiwala P. | IEEE | 2017


    A Low-Complexity Standard-Compliant PAPR Reduction Scheme for OTFS Modulation

    Sharma, Salil / Shah, Syed Waqas Haider / Widmer, Joerg | IEEE | 2024


    Reduction of PAPR using companding with pre-distortion

    You, L. / Xiaolin, Z. / Xin, X. | British Library Online Contents | 2012