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 et al 2011). We show that
In REF , the authors propose a centralized supervised learning approach to train a deep neural network to approximate the resource allocation algorithms.
Learning to optimize: Training deep neural networks for wireless resource management
2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
Computer Science