  This paper studies machine learning-assisted maximum likelihood (ML) and
maximum a posteriori (MAP) receivers for a communication system with memory,
which can be modelled by a trellis diagram. The prerequisite of the ML/MAP
receiver is to obtain the likelihood of the received samples under different
state transitions of the trellis diagram, which relies on the channel state
information (CSI) and the distribution of the channel noise. We propose to
learn the trellis diagram real-time using an artificial neural network (ANN)
trained by a pilot sequence. This approach, termed as the online learning of
trellis diagram (OLTD), requires neither the CSI nor statistics of the noise,
and can be incorporated into the classic Viterbi and the BCJR algorithm.
%Compared with the state-of-the-art ViterbiNet and BCJRNet algorithms in the
literature, it It is shown to significantly outperform the model-based methods
in non-Gaussian channels. It requires much less training overhead than the
state-of-the-art methods, and hence is more feasible for real implementations.
As an illustrative example, the OLTD-based BCJR is applied to a Bluetooth low
energy (BLE) receiver trained only by a 256-sample pilot sequence. Moreover,
the OLTD-based BCJR can accommodate for turbo equalization, while the
state-of-the-art BCJRNet/ViterbiNet cannot. As an interesting by-product, we
propose an enhancement to the BLE standard by introducing a bit interleaver to
its physical layer; the resultant improvement of the receiver sensitivity can
make it a better fit for some Internet of Things (IoT) communications.
