Channel capacity plays a crucial role in the development of modern communication systems as it represents the maximum rate at which information can be reliably transmitted over a communication channel. Nevertheless, for the majority of channels, finding a closed-form capacity expression remains an open challenge. This is because it requires to carry out two formidable tasks a) the computation of the mutual information between the channel input and output, and b) its maximization with respect to the signal distribution at the channel input. In this paper, we address both tasks. Inspired by implicit generative models, we propose a novel cooperative framework to automatically learn the channel capacity, for any type of memory-less channel. In particular, we firstly develop a new methodology to estimate the mutual information directly from a discriminator typically deployed to train adversarial networks, referred to as discriminative mutual information estimator (DIME). Secondly, we include the discriminator in a cooperative channel capacity learning framework, referred to as CORTICAL, where a discriminator learns to distinguish between dependent and independent channel input-output samples while a generator learns to produce the optimal channel input distribution for which the discriminator exhibits the best performance. Lastly, we prove that a particular choice of the cooperative value function solves the channel capacity estimation problem. Simulation results demonstrate that the proposed method offers high accuracy.
翻译:在开发现代通信系统的过程中,频道能力发挥着关键作用,因为它代表了信息能够在通信频道上可靠传输的最大速度,然而,对于大多数频道来说,找到封闭式能力表达方式仍然是一项公开的挑战。这是因为它需要执行两项艰巨的任务:(a) 计算频道输入和输出之间的相互信息,以及(b) 在频道输入的信号分配方面最大限度地扩大渠道能力。在本文件中,我们处理这两个任务。在隐含的变异模型的启发下,我们提议一个新的合作框架,为任何类型的没有记忆的频道自动学习频道能力。特别是,我们首先制定新方法,直接从通常用于培训对抗性网络的受歧视者那里估算相互信息,被称为歧视的相互信息估计器(DIME ) 。第二,我们把歧视者纳入合作性频道能力学习框架中,称为CORTCical, 歧视者学会区分依赖性和独立的频道输入输出样本,而发电机则学会为任何类型的无记忆频道输入能力自动学习最佳传播渠道信息的能力。最后,我们证明,我们展示了一种合作性选择方法,即提供一种高精确性的数据。