  Recently, deep learning-assisted communication systems have achieved many
eye-catching results and attracted more and more researchers in this emerging
field. Instead of completely replacing the functional blocks of communication
systems with neural networks, a hybrid manner of BCJRNet symbol detection is
proposed to combine the advantages of the BCJR algorithm and neural networks.
However, its separate block design not only degrades the system performance but
also results in additional hardware complexity. In this work, we propose a BCJR
receiver for joint symbol detection and channel decoding. It can simultaneously
utilize the trellis diagram and channel state information for a more accurate
calculation of branch probability and thus achieve global optimum with 2.3 dB
gain over separate block design. Furthermore, a dedicated neural network model
is proposed to replace the channel-model-based computation of the BCJR
receiver, which can avoid the requirements of perfect CSI and is more robust
under CSI uncertainty with 1.0 dB gain.
