DNC-Aided SCL-Flip Decoding of Polar Codes

Successive-cancellation list (SCL) decoding of polar codes is promising towards practical adoptions. However, the performance is not satisfactory with moderate code length. Variety of flip algorithms are developed to solve this problem. The key for successful flip is to accurately identify error bit positions. However, state-of-the-art flip strategies, including heuristic and deep-learning-aided (DL-aided) approaches, are not effective in handling long-distance dependencies in sequential SCL decoding. In this work, we propose a new DNC-aided flip decoding with differentiable neural computer (DNC). New action and state encoding are developed for better training and inference efficiency. The proposed method consists of two phases: i) a flip DNC (F-DNC) is exploited to rank most likely flip positions for multi-bit flipping; ii) if multi-bit flipping fails, a flip-validate DNC (FV-DNC) is used to re-select error position and assist single-bit flipping successively. Training methods are designed accordingly for the two DNCs. Simulation results show that proposed DNCaided SCL-Flip (DNC-SCLF) decoding can effectively improve the error-correction performance and reduce number of decoding attempts compared to prior works.