  Soft demodulation is a basic module of traditional communication receivers.
It converts received symbols into soft bits, that is, log likelihood ratios
(LLRs). However, in the nonideal additive white Gaussian noise (AWGN) channel,
it is difficult to accurately calculate the LLR. In this letter, we propose a
demodulator, DemodNet, based on a fully convolutional neural network with
variable input and output length. We use hard bit information to train the
DemodNet, and we propose log probability ratio (LPR) based on the output layer
of the trained DemodNet to realize soft demodulation. The simulation results
show that under the AWGN channel, the performance of both hard demodulation and
soft demodulation of DemodNet is very close to the traditional methods. In
three non-ideal channel scenarios, i.e., the presence of frequency deviation,
additive generalized Gaussian noise (AGGN) channel, and Rayleigh fading
channel, the performance of channel decoding using the soft information LPR
obtained by DemodNet is better than the performance of decoding using the exact
LLR calculated under the ideal AWGN assumption.
