Learning Autonomy in Management of Wireless Random Networks

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

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

This paper presents a machine learning strategy that tackles a distributed optimization task in a wireless network with an arbitrary number of randomly interconnected nodes. Individual nodes decide their optimal states with distributed coordination among other nodes through randomly varying backhaul links. This poses a technical challenge in distributed universal optimization policy robust to a random topology of the wireless network, which has not been properly addressed by conventional deep neural networks (DNNs) with rigid structural configurations. We develop a flexible DNN formalism termed distributed message-passing neural network (DMPNN) with forward and backward computations independent of the network topology. A key enabler of this approach is an iterative message-sharing strategy through arbitrarily connected backhaul links. The DMPNN provides a convergent solution for iterative coordination by learning numerous random backhaul interactions. The DMPNN is investigated for various configurations of the power control in wireless networks, and intensive numerical results prove its universality and viability over conventional optimization and DNN approaches.

Original languageEnglish
Pages (from-to)8039-8053
Number of pages15
JournalIEEE Transactions on Wireless Communications
Volume20
Issue number12
DOIs
Publication statusPublished - 2021 Dec 1

Bibliographical note

Funding Information:
This work was supported in part by the NRF grant funded by the Korea Government Ministry of Science and ICT (MSIT) under Grant 2021R1I1A3054575, in part by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the MSIT (Intelligent 6G Wireless Access System and High Accurate Positioning Enabled MIMO Transmission and Network Technologies for Next 5G-V2X Services) under Grant 2021-0-00467 and Grant 2016-0-00208, and in part by the Korea University Grant, and in part by the National Research Foundation, Singapore and Infocomm Media Development Authority under its Future Communications Research and Development Program, and MOE ARF Tier 2 under Grant T2EP20120-0006.

Publisher Copyright:
© 2002-2012 IEEE.

Keywords

  • Wireless random networks
  • distributed optimization
  • message-passing inference

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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