학술논문
Neural Network-based scheme for PAPR reduction in OFDM Systems
Document Type
Conference
Author
Source
2019 IEEE Fourth Ecuador Technical Chapters Meeting (ETCM) Technical Chapters Meeting (ETCM),2019 IEEE Fourth Ecuador. :1-5 Nov, 2019
Subject
Language
Abstract
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.