  Polar codes have attracted much attention in the past decade due to their
capacity-achieving performance. The higher decoding capacity is required for 5G
and beyond 5G (B5G). Although the cyclic redundancy check (CRC)- assisted
successive cancellation list bit-flipping (CA-SCLF) decoders have been
developed to obtain a better performance, the solution to error bit correction
(bit-flipping) problem is still imperfect and hard to design. In this work, we
leverage the expert knowledge in communication systems and adopt deep learning
(DL) technique to obtain the better solution. A low-complexity long short-term
memory network (LSTM)-assisted CA-SCLF decoder is proposed to further improve
the performance of conventional CA-SCLF and avoid complexity and memory
overhead. Our test results show that we can effectively improve the BLER
performance by 0.11dB compared to prior work and reduce the complexity and
memory overhead by over 30% of the network.
