Learning Optimal Resource Allocations in Wireless Systems
Learning Optimal Resource Allocations in Wireless Systems
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DOI：
10.1109/tsp.2019.2908906
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发表时间：
2019-05-15
影响因子：
5.4
通讯作者：
Ribeiro, Alejandro
中科院分区：
文献类型：
--
作者：
Eisen, Mark;Zhang, Clark;Ribeiro, Alejandro
本文考虑无线通信系统中最优资源分配策略的设计问题，一般将其建模为带随机约束的泛函优化问题。这些优化问题具有学习问题的结构，其中统计损失作为约束出现，激励学习方法的发展来尝试解决它们。为了处理随机约束，在对偶域中进行训练。结果表明，这可以做到这一点时，使用近普遍的学习参数化的最优性损失小。特别是，由于深度神经网络（DNN）几乎是普遍的，因此提倡和探索它们的使用。DNN在这里使用无模型的原始-对偶方法进行训练，该方法同时学习资源分配策略的DNN参数化并优化原始和对偶变量。数值模拟表明，所提出的方法对一些常见的无线资源分配问题的强大性能。
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 (DNNs) 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 parameterization 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.