Constrained Deep Learning for Wireless Resource Management

By Hoon Lee, Sang Hyun Lee, Tony Q. S. Quek. 2019

14 citations recorded by the source corpus.

In this paper, we investigate a deep learning (DL) approach to solve a generic constrained optimization problem in wireless networks, where the objective and constraint functions can be nonconvex. To this target, the computation process of the solution is replaced by deep neural networks (DNNs). The original problem is transformed to a training task of the DNNs subject to nonconvex constraints. Since existing DL libraries are originally intended for unconstrained training, they cannot be directly applied to our constrained training problem. We propose a constrained training strategy based on the primal-dual method from optimization techniques. The proposed DL approach is deployed to solve transmit power allocation problems in various network configurations. The simulation results shed light on the feasibility of the DL method as an alternative to existing optimization algorithms.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Sang Hyun Lee
2. Tony Q. S. Quek
3. Hoon Lee

In Electrical and Electronic Engineering

1. Tony Q. S. Quek
2. Sang Hyun Lee
3. Hoon Lee

Citations in the corpus

• Deep Learning for Distributed Optimization: Applications to Wireless Resource Management (2019, background): “This paper was presented in part at IEEE International Conference on Communications 2019, Shanghai, China, May 2019 [1].”