Belief Propagation Decoding of Polar Codes Using Intelligent Post-Processing

Abstract

Polar code is a channel coding method that has been proved to be able to reach Shannon capacity in the binary discrete memoryless channel. Because of the superior performance and low encoding and decoding complexity, polar code has attracted extensive attention in the industry and been chosen as the channel coding scheme for the control channel in the scenario of EMBB in 5G mobile communication. In this work, we propose an intelligent BP decoding algorithm of polar code based on smart post-processing. We employ the neural network to classify the output data of regular BP decoding into “good-bit” and “bad-bit” categories. We also design a strategy to search the bits, which are most probably incorrect from the “bad-bit” group for post-processing. Then, we can invert the “bad-bit” to correct the residual error in the Belief Propagation (BP) iterative process. Simulation results prove that the proposed algorithm can achieve at least 0.5dB error correction performance enhancement compared with the regular BP decoding with slight computation complexity and energy consumption increase.