Extreme Learning Machine-Based Tone Reservation Scheme for OFDM Systems

Abstract

One of the limitations of the Neural Network-based peak-to-average power ratio (PAPR) reduction methods lies in the long-term training process through a large amount of training data. To address this issue, an extreme learning machine-based (ELM) tone reservation scheme is proposed, which could achieve comparable PAPR performance with low complexity compared to other Neural Network-based algorithms. An ELM-based mapper, trained on the clipping control tone reservation (CC-TR) signals, is designed to reduce the PAPR of orthogonal frequency division multiplexing (OFDM) system. Simulation results indicate that the proposed algorithm owns the advantages of fast learning capability and short training length.