Fast Power Control Adaptation via Meta-Learning for Random Edge Graph Neural Networks

Ivana Nikoloska, Osvaldo Simeone

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Power control in decentralized wireless networks poses a complex stochastic optimization problem when formulated as the maximization of the average sum rate for arbitrary interference graphs. Recent work has introduced data-driven design methods that leverage graph neural network (GNN) to efficiently parametrize the power control policy mapping channel state information (CSI) to the power vector. The specific GNN architecture, known as random edge GNN (REGNN), defines a non-linear graph convolutional architecture whose spatial weights are tied to the channel coefficients, enabling a direct adaption to channel conditions. This paper studies the higher-level problem of enabling fast adaption of the power control policy to time-varying topologies. To this end, we apply first-order meta-learning on data from multiple topologies with the aim of optimizing for a few-shot adaptation to new network configurations.

Original languageEnglish (US)
Title of host publication2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages146-150
Number of pages5
ISBN (Electronic)9781665428514
DOIs
StatePublished - 2021
Externally publishedYes
Event22nd IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2021 - Lucca, Italy
Duration: Sep 27 2021Sep 30 2021

Publication series

NameIEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
Volume2021-September

Conference

Conference22nd IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2021
Country/TerritoryItaly
CityLucca
Period9/27/219/30/21

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Information Systems

Keywords

  • Graph Neural Networks
  • Meta-learning
  • Resource Allocation

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