Deep Learning for Distributed Optimization: Applications to Wireless Resource Management

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

    Research output: Contribution to journalArticlepeer-review

    106 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
    Article number8792179
    Pages (from-to)2251-2266
    Number of pages16
    JournalIEEE Journal on Selected Areas in Communications
    Volume37
    Issue number10
    DOIs
    Publication statusPublished - 2019 Oct

    Bibliographical note

    Funding Information:
    Manuscript received December 15, 2018; revised April 5, 2019; accepted May 20, 2019. Date of publication August 8, 2019; date of current version September 16, 2019. This work was supported in part by the National Research Foundation of Korea (NRF) Grant funded by the Korea Government (MSIT) under Grant 2019R1F1A1060648, in part by Institute of Information & Communications Technology Planning & Evaluation (IITP) Grant funded by the Korea Government (MSIT) [High Accurate Positioning Enabled MIMO Transmission and Network Technologies for Next 5G-V2X (vehicle-to-everything) Services] under Grant 2016-0-00208, in part by the SUTD-ZJU Research Collaboration under Grant SUTD-ZJU/RES/05/2016, and in part by the SUTD AI Program under Grant SGPAIRS1814. This article was presented in part at the IEEE International Conference on Communications, Shanghai, China, May 2019 [1]. (Corresponding author: Sang Hyun Lee.) H. Lee is with the Department of Information and Communications Engineering, Pukyong National University, Busan 48513, South Korea (e-mail: [email protected]).

    Publisher Copyright:
    © 1983-2012 IEEE.

    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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