用于概率成形编码调制的分层分布匹配
Hierarchical Distribution Matching for Probabilistically Shaped Coded Modulation
AI总结：
本文提出面向概率成形编码调制的HiDM方案，以并行接口和流水线架构高效完成DM/invDM，并在相同invDM后BER下支持更高FEC后BER，代价是略高速率损失与信噪比需求。
摘要：
将用于概率成形（PS）的分布匹配（DM）与逆匹配（invDM）同软判决前向纠错（FEC）编码相结合，其实现难点可通过反向级联来缓解；在该结构中，FEC编码与解码位于成形算法内部。PS看似能够取得接近Shannon极限的性能，但仍存在需要谨慎处理的实际实现挑战。我们提出一种分层DM（HiDM）方案，它具有完全并行化的输入/输出接口和流水线架构，能够高效执行DM/invDM，而无需诸如恒成分DM（CCDM）等先前所提方法中的复杂运算。此外，在相同的invDM后误码率（BER）性能下，HiDM可以在显著更高的FEC后BER下工作，这为仿真提供了便利。这些优势的代价是略大的速率损失，以及在给定FEC后BER下所需信噪比略高。
英文摘要：
The implementation difficulties of combining distribution matching (DM) and dematching (invDM) for probabilistic shaping (PS) with soft-decision forward error correction (FEC) coding can be relaxed by reverse concatenation, for which the FEC coding and decoding lies inside the shaping algorithms. PS can seemingly achieve performance close to the Shannon limit, although there are practical implementation challenges that need to be carefully addressed. We propose a hierarchical DM (HiDM) scheme, having fully parallelized input/output interfaces and a pipelined architecture that can efficiently perform the DM/invDM without the complex operations of previously proposed methods such as constant composition DM (CCDM). Furthermore, HiDM can operate at a significantly larger post-FEC bit error rate (BER) for the same post-invDM BER performance, which facilitates simulations. These benefits come at the cost of a slightly larger rate loss and required signal-to-noise ratio at a given post-FEC BER.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
