  In this paper, a neural network-aided bit-interleaved coded modulation
(NN-BICM) receiver is designed to mitigate the nonlinear clipping distortion in
the LDPC coded direct currentbiased optical orthogonal frequency division
multiplexing (DCOOFDM) systems. Taking the cross-entropy as loss function, a
feed forward network is trained by backpropagation algorithm to output the
condition probability through the softmax activation function, thereby
assisting in a modified log-likelihood ratio (LLR) improvement. To reduce the
complexity, this feed-forward network simplifies the input layer with a single
symbol and the corresponding Gaussian variance instead of focusing on the
inter-carrier interference between multiple subcarriers. On the basis of the
neural network-aided BICM with Gray labelling, we propose a novel stacked
network architecture of the bitinterleaved coded modulation with iterative
decoding (NN-BICMID). Its performance has been improved further by calculating
the condition probability with the aid of a priori probability that derived
from the extrinsic LLRs in the LDPC decoder at the last iteration, at the
expense of customizing neural network detectors at each iteration time
separately. Utilizing the optimal DC bias as the midpoint of the dynamic
region, the simulation results demonstrate that both the NN-BICM and NN-BICM-ID
schemes achieve noticeable performance gains than other counterparts, in which
the NN-BICM-ID clearly outperforms NN-BICM with various modulation and coding
schemes.
