  A deep-learning-aided successive-cancellation list (DL-SCL) decoding
algorithm for polar codes is introduced with deep-learning-aided
successive-cancellation (DL-SC) decoding being a specific case of it. The
DL-SCL decoder works by allowing additional rounds of SCL decoding when the
first SCL decoding attempt fails, using a novel bit-flipping metric. The
proposed bit-flipping metric exploits the inherent relations between the
information bits in polar codes that are represented by a correlation matrix.
The correlation matrix is then optimized using emerging deep-learning
techniques. Performance results on a polar code of length 128 with 64
information bits concatenated with a 24-bit cyclic redundancy check show that
the proposed bit-flipping metric in the proposed DL-SCL decoder requires up to
66% fewer multiplications and up to 36% fewer additions, without any need to
perform transcendental functions, and by providing almost the same
error-correction performance in comparison with the state of the art.
