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

TY - CHAP

T1 - Fast Power Control Adaptation via Meta-Learning for Random Edge Graph Neural Networks

AU - Nikoloska, Ivana

AU - Simeone, Osvaldo

N1 - Funding Information: This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Program (Grant Agreement No. 725731). Funding Information: This work was supported by the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Program (Grant Agreement No. 725731). Publisher Copyright: © 2021 IEEE.

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Graph Neural Networks

KW - Meta-learning

KW - Resource Allocation

UR - http://www.scopus.com/inward/record.url?scp=85114699906&partnerID=8YFLogxK

U2 - 10.1109/SPAWC51858.2021.9593131

DO - 10.1109/SPAWC51858.2021.9593131

M3 - Conference paper

AN - SCOPUS:85114699906

T3 - IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC

SP - 146

EP - 150

BT - 2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 22nd IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2021

Y2 - 27 September 2021 through 30 September 2021

ER -