Channel capacity estimation plays a crucial role in beyond 5G intelligent communications. Despite its significance, this task is challenging for a majority of channels, especially for the complex channels not modeled as the well-known typical ones. Recently, neural networks have been used in mutual information estimation and optimization. They are particularly considered as efficient tools for learning channel capacity. In this paper, we propose a cooperative framework to simultaneously estimate channel capacity and design the optimal codebook. First, we will leverage MIM-based GAN, a novel form of generative adversarial network (GAN) using message importance measure (MIM) as the information distance, into mutual information estimation, and develop a novel method, named MIM-based mutual information estimator (MMIE). Then, we design a generalized cooperative framework for channel capacity learning, in which a generator is regarded as an encoder producing the channel input, while a discriminator is the mutual information estimator that assesses the performance of the generator. Through the adversarial training, the generator automatically learns the optimal codebook and the discriminator estimates the channel capacity. Numerical experiments will demonstrate that compared with several conventional estimators, the MMIE achieves state-of-the-art performance in terms of accuracy and stability.

翻译：信道容量估计在超5G智能通信中起着关键作用。尽管其重要性显著，但对于大多数信道（尤其是那些未被建模为经典典型信道的复杂信道）而言，这一任务仍具挑战性。近期，神经网络已被应用于互信息估计与优化，并被视为学习信道容量的有效工具。本文提出一种协同框架，可同时实现信道容量估计与最优码本设计。首先，我们将基于MIM的生成对抗网络（MIM-based GAN）——一种采用消息重要性度量（MIM）作为信息距离的新型生成对抗网络——引入互信息估计，并开发出一种名为基于MIM的互信息估计器（MMIE）的新方法。随后，我们设计了一个通用的信道容量学习协同框架，其中生成器被视为产生信道输入的编码器，而判别器则是评估生成器性能的互信息估计器。通过对抗训练，生成器自动学习最优码本，判别器则估计信道容量。数值实验表明，与若干传统估计器相比，MMIE在准确性与稳定性方面均达到了最优性能。