机器学习能教我们什么关于通信？
What Can Machine Learning Teach Us about Communications?
摘要：
在过去十年中，机器学习的快速进步开始产生深远的影响。对于通信，领域专业知识有限的工程师现在可以使用现成的学习包来设计基于模拟的高性能系统。在当前机器学习革命之前，大多数通信工程师都非常清楚系统参数（如滤波器系数）可以使用随机梯度下降来学习。然而，还不清楚系统结构中更复杂的部分是否也可以学习。在本文中，我们讨论了机器学习技术的应用，以两个通信问题，并专注于什么可以学到的系统。令我们惊喜的是，在一个例子中观察到的收益有一个简单的解释，只有在事后才变得清晰。从本质上讲，深度学习发现了一种简单而有效的策略，这是以前没有考虑过的。
英文摘要：
Rapid improvements in machine learning over the past decade are beginning to have far-reaching effects. For communications, engineers with limited domain expertise can now use off-the-shelf learning packages to design high-performance systems based on simulations. Prior to the current revolution in machine learning, the majority of communication engineers were quite aware that system parameters (such as filter coefficients) could be learned using stochastic gradient descent. It was not at all clear, however, that more complicated parts of the system architecture could be learned as well. In this paper, we discuss the application of machine-learning techniques to two communications problems and focus on what can be learned from the resulting systems. We were pleasantly surprised that the observed gains in one example have a simple explanation that only became clear in hindsight. In essence, deep learning discovered a simple and effective strategy that had not been considered earlier.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
