This study focuses on the efficiency of message-passing-based decoding algorithms for polar and low-density parity-check (LDPC) codes. Both successive cancellation (SC) and belief propagation (BP) decoding algorithms are studied {in} the message-passing framework. Counter-intuitively, SC decoding demonstrates the highest decoding efficiency, although it was considered a weak decoder {in terms of} error-correction performance. We analyze the complexity-performance tradeoff to dynamically track the decoding efficiency, where the complexity is measured by the number of messages passed (NMP), and the performance is measured by the statistical distance to the maximum a posteriori (MAP) estimate. This study offers a new insight into the contribution of each message passed in decoding, and compares various decoding algorithms on a message-by-message level. The analysis corroborates recent results on terabits-per-second polar SC decoders, and might shed light on better scheduling strategies.

翻译：本研究聚焦于极化和低密度奇偶校验（LDPC）码中基于消息传递的译码算法的效率。在消息传递框架下，研究了连续消除（SC）和置信传播（BP）两种译码算法。反直觉的是，SC译码显示出最高的译码效率，尽管其在纠错性能方面曾被视为弱译码器。我们分析复杂度-性能权衡以动态跟踪译码效率，其中复杂度以传递消息数（NMP）衡量，性能以与最大后验（MAP）估计的统计距离衡量。本研究为译码中每条传递消息的贡献提供了新见解，并在逐消息层面上比较了各种译码算法。该分析验证了近期关于太比特每秒极化SC译码器的结果，并可能为更优的调度策略提供启示。