This paper examines the maximum code rate achievable by a data-driven communication system over some unknown discrete memoryless channel in the finite blocklength regime. A class of channel codes, called learning-based channel codes, is first introduced. Learning-based channel codes include a learning algorithm to transform the training data into a pair of encoding and decoding functions that satisfy some statistical reliability constraint. Data-dependent achievability and converse bounds in the non-asymptotic regime are established for this class of channel codes. It is shown analytically that the asymptotic expansion of the bounds for the maximum achievable code rate of the learning-based channel codes are tight for sufficiently large training data.

翻译：本文研究了在有限码长条件下，针对未知离散无记忆信道，数据驱动通信系统所能达到的最大码率问题。首先引入了一类名为基于学习的信道编码的信道编码方法。此类信道编码包含一种学习算法，能够将训练数据转化为满足特定统计可靠性约束的编码与解码函数对。针对这类信道编码，建立了非渐近条件下的数据依赖性可达性及逆界，并通过理论分析表明，当训练数据量足够大时，基于学习的信道编码最大可达码率边界的渐近展开是紧致的。