Low PAPR waveform design for OFDM SYSTEM based on Convolutional Auto-Encoder
This addresses PAPR reduction for OFDM systems, which is an incremental improvement in wireless communication signal processing.
The paper tackles the problem of reducing peak-to-average power ratio (PAPR) in OFDM systems by proposing a convolutional autoencoder architecture with a PAPR reduction block and HPA model, achieving performance improvements in BER, PAPR, and spectral response compared to common algorithms.
This paper introduces the architecture of a convolutional autoencoder (CAE) for the task of peak-to-average power ratio (PAPR) reduction and waveform design, for orthogonal frequency division multiplexing (OFDM) systems. The proposed architecture integrates a PAPR reduction block and a non-linear high power amplifier (HPA) model. We apply gradual loss learning for multi-objective optimization. We analyze the models performance by examining the bit error rate (BER), the PAPR and the spectral response, and comparing them with common PAPR reduction algorithms.