Neural Network-based scheme for PAPR reduction in OFDM Systems

Diego Reinoso-Chisaguano, Felipe Grijalva, Martha Cecilia Paredes Paredes, Jorge Carvajal-Rodriguez

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Resumen

This paper proposes a neural network-based scheme for Peak-to-Average Power Ratio (PAPR) reduction which also replaces the Inverse Fast Fourier Transform (IFFT) block of an Orthogonal Frequency Division Multiplexing (OFDM) transmitter. The scheme is composed by one neural network per subcarrier, which are implemented only in the transmitter. The training inputs of each neural network are frequency-domain OFDM symbols and the outputs are time-domain PAPR reduced OFDM symbols obtained using a Branch-and-Bound Constellation Extension (BBCE) scheme. The results show that our scheme achieves a PAPR reduction and Bit Error Rate (BER) similar to constellation shaping techniques but with reduced complexity.

Idioma originalInglés
Título de la publicación alojada2019 IEEE 4th Ecuador Technical Chapters Meeting, ETCM 2019
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781728137643
DOI
EstadoPublicada - nov. 2019
Publicado de forma externa
Evento4th IEEE Ecuador Technical Chapters Meeting, ETCM 2019 - Guayaquil, Ecuador
Duración: 13 nov. 201915 nov. 2019

Serie de la publicación

Nombre2019 IEEE 4th Ecuador Technical Chapters Meeting, ETCM 2019

Conferencia

Conferencia4th IEEE Ecuador Technical Chapters Meeting, ETCM 2019
País/TerritorioEcuador
CiudadGuayaquil
Período13/11/1915/11/19

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