Post-Processing Methods for Improving Coding Gain in Belief Propagation Decoding of Polar Codes

Abstract

Belief propagation (BP) is a high-throughput, low- latency decoding algorithm for polar codes, but the error-correcting performance is known to be inferior than successive cancellation (SC) decoding. To improve the error-correcting performance of BP decoding, we design post- processing methods targeting false converged errors, oscillation errors, and unconverged errors that determine the performance of BP decoding. False convergence can be resolved by perturbing, or gradually freezing the information bits, followed by error cleanup using BP. Oscillations can be resolved by enhancing the stable bits and perturbing the unstable bits, followed by error cleanup using BP. Unconverged errors can be resolved by enhancing the reliably stable bits and weakening the unstable bits. Results show that the error rates of BP decoding can be improved by an order of magnitude or more, allowing it to overtake SC in error rate and coding gain. Post-processing can be implemented very efficiently, costing less than 4.3% overhead in silicon area, and it does not affect the throughput or latency of BP decoding.