Convolutional Neural Network-aided Bit-flipping for Belief Propagation Decoding of Polar Codes
This work addresses the need for low-latency, high-throughput decoding in 5G communications, representing an incremental improvement over prior bit-flipping techniques.
The paper tackles the high latency and error rate of belief propagation decoding for polar codes by proposing a convolutional neural network-assisted bit-flipping mechanism, which achieves higher prediction accuracy and better error correction than existing methods with half the latency and a lower block error rate than SC list decoding.
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).