In this paper, we investigate an artificial-intelligence (AI) driven approach to design error correction codes (ECC). Classic error-correction code design based upon coding-theoretic principles typically strives to optimize some performance-related code property such as minimum Hamming distance, decoding threshold, or subchannel reliability ordering. In contrast, AI-driven approaches, such as reinforcement learning (RL) and genetic algorithms, rely primarily on optimization methods to learn the parameters of an optimal code within a certain code family. We employ a constructor-evaluator framework, in which the code constructor can be realized by various AI algorithms and the code evaluator provides code performance metric measurements. The code constructor keeps improving the code construction to maximize code performance that is evaluated by the code evaluator. As examples, we focus on RL and genetic algorithms to construct linear block codes and polar codes. The results show that comparable code performance can be achieved with respect to the existing codes. It is noteworthy that our method can provide superior performances to classic constructions in certain cases (e.g., list decoding for polar codes).

中文翻译：

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AI 编码：学习构建纠错码

在本文中，我们研究了一种设计纠错码 (ECC) 的人工智能 (AI) 驱动方法。基于编码理论原理的经典纠错码设计通常致力于优化一些与性能相关的代码属性，例如最小汉明距离、解码阈值或子信道可靠性排序。相比之下，人工智能驱动的方法，如强化学习 (RL) 和遗传算法，主要依靠优化方法来学习特定代码家族中最优代码的参数。我们采用构造函数-评估器框架，其中代码构造函数可以通过各种 AI 算法实现，代码评估器提供代码性能指标测量。代码构造器不断改进代码构造以最大化代码评估器评估的代码性能。例如，我们专注于 RL 和遗传算法来构建线性分组码和极性码。结果表明，相对于现有代码，可以实现可比较的代码性能。值得注意的是，我们的方法在某些情况下（例如，极性码的列表解码）可以提供优于经典结构的性能。