Learning Optimal Resource Allocations in Wireless Systems
This work addresses resource allocation challenges in wireless communication systems, offering a novel learning-based approach that is incremental in applying existing methods to this domain.
The paper tackles the problem of designing optimal resource allocation policies in wireless systems by formulating it as a functional optimization with stochastic constraints, and proposes a model-free primal-dual method using deep neural networks to learn these policies, demonstrating strong performance in numerical simulations.
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.