AI 中文总结
研究针对BCH码提出准BP解码方案，利用码自同构等优化结构，引入基于神经网络的变体消除复杂函数，仿真表明该方案误帧率性能有竞争力，与其他方法级联可缩小与最大似然界差距，为BCH码解码提供可行途径。
AI 中文摘要
本文提出了一种用于BCH码的准BP解码方案，该方案在利用码自同构和优化冗余校验矩阵的同时，保留了信念传播的可并行结构。为消除校验节点更新中计算昂贵的双曲正切和反双曲正切函数，引入基于神经网络的变体，用在三重约束损失函数下训练的轻量级卷积神经网络替代它们，以强制非负性和顺序一致性。对三种BCH码的仿真结果表明，准BP解码实现了有竞争力的误帧率性能，与类似码长的LDPC码的信念传播解码相比，差距在0.25分贝以内。基于神经网络的变体性能损失可忽略不计，同时能在硬件加速器上通过算术运算实现稳定部署。与有序统计解码变体级联进一步缩小了与最大似然界的差距。因此，所提方案为下一代通信系统中BCH码的高通量、低延迟解码提供了一条可行途径。
英文摘要
This paper proposes a quasi-BP decoding scheme for BCH codes that preserves the parallelizable structure of belief propagation while exploiting code automorphisms and optimized redundant parity-check matrices. To eliminate the computationally expensive $\tanh$ and $\tanh^{-1}$ functions in check node updates, we further introduce a neural-network-based variant that replaces them with a lightweight convolutional neural network trained under a triple-constraint loss function enforcing non-negativity and order consistency. Simulation results for three BCH codes demonstrate that quasi-BP decoding achieves competitive frame error rate performance, with a gap within 0.25 decibels compared with belief propagation decoding of an LDPC code of similar blocklength. The neural-network-based variant incurs negligible performance loss while enabling stable deployment with arithmetic operations on hardware accelerators. Concatenation with an ordered statistics decoding variant further bridges the gap to the maximum-likelihood bound. Hence, the proposed schemes offer a viable path toward high-throughput, low-latency decoding of BCH codes in next-generation communication systems.
Comments6 pages, 3 figures, 1 table
