A novel PAPR reduction scheme based on time-delay neural network for CO-OFDM systems

Abstract

In this paper, we propose a low-complexity peak-to-average power ratio (PAPR) reduction scheme based on time-delay neural network (TDNN) for coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems to reduce the limitation of PAPR on the total power of laser diodes and the impact of fiber nonlinear effects. The proposed scheme is jointly trained on traditional iterative clipping and filtering (ICF) scheme and simplified clipping and filtering (SCF) scheme, and changes the objective function by adjusting the given training weight α. Compared to the original orthogonal frequency division multiplexing (OFDM) signal of existing system, our method achieves an adjustable PAPR reduction of 5.31 dB to 6.12 dB for 10−4 complementary cumulative distribution function (CCDF). When bit error rate (BER) Pe=10−3, the proposed scheme (α=0.5) achieves additional transmission distances of 220 km, 55 km, 105 km, and 100 km, compared to the original OFDM signal, partial transmission sequence (PTS) scheme, ICF scheme, and feedforward neural network (FNN)-based ICF scheme, respectively. Besides, our method achieves 86.3% and 80.0% floating-point operations (FLOPs) reductions, compared with PTS scheme and ICF scheme, respectively, which effectively reduces the complexity of PAPR reduction module, saving the system computational time.