并行信道与有记忆信道的散度最小化分布匹配
Divergence-Minimizing Distribution Matching for Parallel Channels and Channels with Memory
- Technical University of Munich（慕尼黑工业大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
将散度最小化分布匹配扩展到有记忆分布，证明其选择最高概率序列，应用于并行AWGN信道，联合ESS相比乘积DM大幅降低短块长度下的速率损失。
AI中文摘要：
分布匹配器（DM）的理论被扩展到具有记忆的分布。散度最小化DM被证明会选择具有最高目标概率的序列，与无记忆情况相同。散度的标度律被扩展到由创新过程驱动的分布。该理论被应用于并行加性高斯白噪声信道。在实现中使用了具有加权能量约束的改进枚举球形整形（ESS）方法。一个包含三个信道的示例表明，在短块长度下，跨信道联合ESS相比乘积DM将速率损失降低了很大倍数。通过概率幅度整形和5G-NR低密度奇偶校验码的仿真证实了这些增益。
英文摘要：
Theory for distribution matchers (DMs) is extended to distributions with memory. The divergence-minimizing DM is shown to select the sequences with the highest target probability, as in the memoryless case. A scaling law for divergence is extended to distributions driven by innovation processes. The theory is applied to parallel additive white Gaussian noise channels. A modified enumerative sphere-shaping (ESS) method with a weighted energy constraint is used in implementations. An illustrative example with three channels shows that joint ESS across channels reduces the rate loss by a large factor compared to product DMs at short blocklengths. The gains are confirmed by simulations with probabilistic amplitude shaping and a 5G-NR low-density parity-check code.
