Deep Learning for Distributed Optimization: Applications to Wireless Resource Management

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

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

This paper studies a deep learning (DL) framework to solve distributed non-convex constrained optimizations in wireless networks where multiple computing nodes, interconnected via backhaul links, desire to determine an efficient assignment of their states based on local observations. Two different configurations are considered: First, an infinite-capacity backhaul enables nodes to communicate in a lossless way, thereby obtaining the solution by centralized computations. Second, a practical finite-capacity backhaul leads to the deployment of distributed solvers equipped along with quantizers for communication through capacity-limited backhaul. The distributed nature and the non-convexity of the optimizations render the identification of the solution unwieldy. To handle them, deep neural networks (DNNs) are introduced to approximate an unknown computation for the solution accurately. In consequence, the original problems are transformed to training tasks of the DNNs subject to non-convex constraints where existing DL libraries fail to extend straightforwardly. A constrained training strategy is developed based on the primal-dual method. For distributed implementation, a novel binarization technique at the output layer is developed for quantization at each node. Our proposed distributed DL framework is examined in various network configurations of wireless resource management. Numerical results verify the effectiveness of our proposed approach over existing optimization techniques.

Original languageEnglish
JournalIEEE Journal on Selected Areas in Communications
DOIs
Publication statusPublished - 2019 Jan 1

Keywords

  • Deep neural network
  • distributed deep learning
  • primal-dual method
  • wireless resource management.

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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