# Learning to Optimize: Training Deep Neural Networks for Wireless Resource Management

## TL;DR

This paper introduces a deep learning approach to approximate complex wireless resource management algorithms, enabling near real-time performance with high accuracy and significant speed improvements over traditional optimization methods.

## Contribution

It provides a theoretical framework for learning optimization algorithms with DNNs and demonstrates practical efficiency in approximating power allocation algorithms like WMMSE.

## Key findings

- DNNs can accurately approximate WMMSE with mild error dependence on network size.
- DNN-based methods achieve orders of magnitude faster computation.
- Theoretical analysis confirms learnability of certain optimization algorithms.

## Abstract

For the past couple of decades, numerical optimization has played a central role in addressing wireless resource management problems such as power control and beamformer design. However, optimization algorithms often entail considerable complexity, which creates a serious gap between theoretical design/analysis and real-time processing. To address this challenge, we propose a new learning-based approach. The key idea is to treat the input and output of a resource allocation algorithm as an unknown non-linear mapping and use a deep neural network (DNN) to approximate it. If the non-linear mapping can be learned accurately by a DNN of moderate size, then resource allocation can be done in almost real time -- since passing the input through a DNN only requires a small number of simple operations. In this work, we address both the thereotical and practical aspects of DNN-based algorithm approximation with applications to wireless resource management. We first pin down a class of optimization algorithms that are `learnable' in theory by a fully connected DNN. Then, we focus on DNN-based approximation to a popular power allocation algorithm named WMMSE (Shi {\it et al} 2011). We show that using a DNN to approximate WMMSE can be fairly accurate -- the approximation error $\epsilon$ depends mildly [in the order of $\log(1/\epsilon)$] on the numbers of neurons and layers of the DNN. On the implementation side, we use extensive numerical simulations to demonstrate that DNNs can achieve orders of magnitude speedup in computational time compared to state-of-the-art power allocation algorithms based on optimization.