PAPR Reduction Method of FBMC/OQAM System Based on Real Valued Neural Network

Filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) is one of the candidate schemes for 5G multicarrier communication system. Like orthogonal frequency division multiplexing (OFDM) and other multicarrier schemes, it has the problem of high peak-to-average power ratio (PAPR), which will affect the efficiency of high power amplifier (HPA). Aiming at the problem of too high PAPR in FBMC/OQAM system, this paper proposes a method based on real valued neural network. By establishing two real valued neural networks are established at the transmitter and the receiver, this method can reduce PAPR and bit error ratio (BER). It is shown via simulation results that compared with the dispersive selected mapping (DSLM), clipping, coding, and PRnet, the proposed method can attain better performance in PAPR and BER.