用强化学习设计通用极化码序列，支持6G长码长并提升性能。
A Reinforcement Learning Based Universal Sequence Design for Polar Codes
- 基于强化学习构建可扩展的极化码序列设计框架。
- 在2048码长下比基准方案提升0.2 dB，5G配置下表现不逊于现行标准。
- 适合通信系统设计者及6G极化码研究者参考。
为推进极化码在6G中的应用，我们提出一种基于强化学习的通用序列设计框架，该框架具备可扩展性和对不同信道条件与译码策略的适应性。关键优势在于可支持高达2048的码长，适用于标准化。在5G支持的所有(N,K)配置下，本方法性能与5G NR序列相当，并在N=2048时相比beta-expansion基线获得最高0.2 dB增益。我们进一步揭示了实现大规模学习的关键要素：(i) 基于极化码通用偏序性质的物理规律约束学习；(ii) 利用决策的弱长期影响以限制前瞻评估；(iii) 联合多配置优化以提升学习效率。
原文摘要 · Abstract (English)
To advance Polar code design for 6G applications, we develop a reinforcement learning-based universal sequence design framework that is extensible and adaptable to diverse channel conditions and decoding strategies. Crucially, our method scales to code lengths up to $2048$, making it suitable for use in standardization. Across all $(N,K)$ configurations supported in 5G, our approach achieves competitive performance relative to the NR sequence adopted in 5G and yields up to a 0.2 dB gain over the beta-expansion baseline at $N=2048$. We further highlight the key elements that enabled learning at scale: (i) incorporation of physical law constrained learning grounded in the universal partial order property of Polar codes, (ii) exploitation of the weak long term influence of decisions to limit lookahead evaluation, and (iii) joint multi-configuration optimization to increase learning efficiency.
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