  Soft demodulation, or demapping, of received symbols back into their conveyed
soft bits, or bit log-likelihood ratios (LLRs), is at the very heart of any
modern receiver. In this paper, a trainable universal neural network-based
demodulator architecture, dubbed "LLRnet", is introduced. LLRnet facilitates an
improved performance with significantly reduced overall computational
complexity. For instance for the commonly used quadrature amplitude modulation
(QAM), LLRnet demonstrates LLR estimates approaching the optimal log maximum
a-posteriori inference with an order of magnitude less operations than that of
the straightforward exact implementation. Link-level simulation examples for
the application of LLRnet to 5G-NR and DVB-S.2 are provided. LLRnet is a (yet
another) powerful example for the usefulness of applying machine learning to
physical layer design.
