  Known for their capacity-achieving abilities, polar codes have been selected
as the control channel coding scheme for 5G communications. To satisfy the
needs of high throughput and low latency, belief propagation (BP) is chosen as
the decoding algorithm. However, in general, the error performance of BP is
worse than that of enhanced successive cancellation (SC). Recently,
critical-set bit-flipping (CS-BF) is applied to BP decoding to lower the error
rate. However, its trial and error process result in even longer latency. In
this work, we propose a convolutional neural network-assisted bit-flipping
(CNN-BF) mechanism to further enhance BP decoding of polar codes. With
carefully designed input data and model architecture, our proposed CNN-BF can
achieve much higher prediction accuracy and better error correction capability
than CS-BF but with only half latency. It also achieves a lower block error
rate (BLER) than SC list (CA-SCL).
