Deep neural networks (DNNs) have been applied to address various wireless tasks, which usually need a large number of samples for training. Considering that wireless networks are highly dynamic and gathering data is expensive, it is paramount to reduce the sample complexity. Incorporating domain knowledge into learning is a promising way of decreasing training samples. Yet how to invoke priori knowledge of wireless tasks for efficient data representation remains largely unexplored. In this article, we first briefly summarize several approaches to address training complexity. Then, we show that a symmetric property, permutation equivariance, widely exists in wireless tasks. We introduce a simple method to compress the training set by exploiting such a generic prior, which is to jointly sort the input and output of the DNNs. We use interference coordination and caching policy optimization to illustrate how to apply this method of data representation, i.e., ranking, and how much the sample complexity can be reduced. Simulation results demonstrate that the training samples required to achieve the same performance as the traditional data representation can be reduced by $10 \sim 200$ folds by ranking.

本文介绍将对称先验，即排列等变性，与功率分配、缓存算法和干扰协调等无线任务相结合，通过排名来改善深度神经网络训练的效率，而不使用先验时所需的训练样本，DNN中的自由参数和训练时间可以大大减少。

使用对称先验提升无线资源分配的学习效率