Noise equalization scheme based on probabilistic shaping and complex-valued ANN for dual-polarization continuous spectrum NFDM system with high-order modulation formats

Abstract

In dual-polarization continuous spectrum nonlinear frequency division multiplexing (DP-CS NFDM) systems, the presence of noise destroys the independence between subcarriers in the nonlinear spectral domain (NSD). Additionally, as the modulation order and the number of subcarriers increase, the signal power increases and the effect of noise on the performance of the system becomes more severe. In this paper, we propose a noise equalization scheme based on probabilistic shaping (PS) and complex-valued neural network (c-ANN) for dual-polarization transmission CS NFDM system with high-power signals. The scheme utilizes PS to reduce the distribution probability of high-power constellation points of the signal with high-order modulation format, to decrease the peak-to-average power ratio of the continuous spectrum subcarriers and the average power of the signal, which can achieve the suppression and equalization of the amplifier spontaneous emission (ASE) noise and the processing noise. In addition, the scheme uses the c-ANN to effectively reduce the correlation between subcarriers caused by the noise and improve the system performance. The effectiveness of the proposed scheme is verified in the DP-CS NFDM system, and the simulation results show that the Q-factor gain is enhanced by roughly 1.1 dB and 0.8 dB for the number of subcarriers of 128 and 256, respectively, compared to the classic nonlinear Fourier transform (NFT) scheme. Among them, with different noise figure (NF) and satisfying the 7% forward error correction (FEC) threshold, the 512QAM signal with an entropy of 8 after equalization increase the transmission distance by 80 km longer than that of uniform 256QAM signals for the number of subcarriers of 128. Compared with the joint scheme based on PS and real-valued neural network (r-ANN), this scheme has better equalization performance at the same complexity, and the complexity can be reduced by 50% under the same performance.