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

Low-Complexity LSTM-Assisted Bit-Flipping Algorithm for Successive Cancellation List Polar Decoder