High-Throughput and Energy-Efficient Belief Propagation Polar Code Decoder

Owing to their capacity-achieving performance and low encoding and decoding complexity, polar codes have received significant attention recently. Successive cancellation decoding (SCD) and belief propagation decoding (BPD) are two popular approaches for decoding polar codes. SCD, despite having less computational complexity when compared with BPD, suffers from long latency due to the serial nature of the SC algorithm. BPD, on the other hand, is parallel in nature and is more attractive for low-latency applications. However, due to the iterative nature of BPD, the required latency and energy dissipation increase linearly with the number of iterations. In this paper, we propose a novel scheme based on subfactor-graph freezing to reduce the average number of computations as well as the average number of iterations required by BPD, which directly translates into lower latency and energy dissipation. Simulation results show that the proposed scheme has no performance degradation and achieves significant reduction in computation complexity over the existing methods. Moreover, the hardware architecture for the proposed scheme is developed and compared with the state-of-the-art BPD implementations for (1024, 512) polar codes. A decoding throughput of 13.9 Gb/s is achieved along with a 60%-73% improvement in energy reduction and two times increase in hardware efficiency when compared with the existing BPD implementations.