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.
PAPR Reduction Scheme for Deep Learning-Based Communication Systems Using Autoencoders
01.05.2020
229580 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Deep Tracking Portfolios Using Autoencoders and Variational Autoencoders
Springer Verlag | 2024
|Pilot-Assisted PAPR Reduction Technique for Optical OFDM Communication Systems
British Library Online Contents | 2014
|Reduction of PAPR using companding with pre-distortion
British Library Online Contents | 2012
|