Efficient Belief Propagation List Decoding of Polar Codes As known to all, polar codes have been chosen as the control channel codes for the enhance mobile broadband (eMBB) scenario in the 3GPP RAN1. Due to its excellent performance, polar codes also have caused widespread concern in the academia and industry. In general, polar codes can be decoded by two methods: successive cancellation (SC) and belief propagation (BP) algorithms. However, compared with the series of SC algorithms, the errorcorrection performance of BP decoding is not satisfactory, even though it has excellent parallel throughput. Hence, this paper proposes an efficient BP list (EBPL) decoding of polar codes which can enhance the performance to the same scale of successive cancellation list (SCL) without sacrificing decoding throughput. With reasonable complexity, the proposed EBPL decoding can achieve comparable BER and FER compared to SCL in simulation results.

Flexible and Adaptive Path Splitting of Simplified Successive Cancellation List Polar Decoding Polar codes are the first channel codes that have been proven to achieve the Shannon limit. In order to achieve satisfactory performance in moderate code length cases, successive cancellation list (SCL) decoding is considered as a promising scheme, which could achieve the performance of maximum likelihood decoding as list size increases. However, it suffers from the long latency in practical application, herein, simplified successive cancellation list (SSCL) can be employed to reduce the latency by parallel decoding of some special constituent nodes. In this paper, we focus on finding a way of path splitting during SSCL decoding. Based on the path metric (PM), an adaptive method is proposed to tighten the exact bound of path splitting without performance degradation. Compared with constant path splitting method, the proposed algorithm shows advantages in both performance and complexity reduction. Also, it avoids any foreknowledge soft information as a threshold stored in memory, which is different from the existing adaptive methods.