This paper considers the design of optimal resource allocation policies in wireless communication systems which are generically modeled as a functional optimization problem with stochastic constraints. These optimization problems have the structure of a learning problem in which the statistical loss appears as a constraint, motivating the development of learning methodologies to attempt their solution. To handle stochastic constraints, training is undertaken in the dual domain. It is shown that this can be done with small loss of optimality when using near-universal learning parameterizations. In particular, since deep neural networks (DNN) are near-universal their use is advocated and explored. DNNs are trained here with a model-free primal-dual method that simultaneously learns a DNN parametrization of the resource allocation policy and optimizes the primal and dual variables. Numerical simulations demonstrate the strong performance of the proposed approach on a number of common wireless resource allocation problems.
翻译:本文审议了无线通信系统的最佳资源分配政策的设计,这些系统一般模拟为功能优化问题,具有随机限制,优化问题具有学习问题的结构,统计损失似乎是一个制约因素,鼓励制定学习方法,试图解决这些问题。为了处理随机限制,培训在双重领域进行,显示在使用近乎普遍学习参数时,可以略微失去最佳性。特别是,由于深神经网络(DNN)几乎普遍使用,因此宣传和探索了这些网络。