Complexity Suppression of AutoEncoder for PAPR Reduction of OFDM Signals | IEEE Conference Publication | IEEE Xplore

Complexity Suppression of AutoEncoder for PAPR Reduction of OFDM Signals


Abstract:

Orthogonal frequency division multiplexing (OFDM) signals have the characteristic of a high peak-to-average power ratio (PAPR). In this paper, we consider a method to sup...Show More

Abstract:

Orthogonal frequency division multiplexing (OFDM) signals have the characteristic of a high peak-to-average power ratio (PAPR). In this paper, we consider a method to suppress the computational complexity of AutoEncoder, which is a deep learning model, to reduce the PAPR of OFDM signals. We also evaluate the effectiveness of the method through numerical experiments.
Date of Conference: 18-21 October 2022
Date Added to IEEE Xplore: 18 January 2023
ISBN Information:
Print on Demand(PoD) ISSN: 2378-8143
Conference Location: Osaka, Japan

References

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