University of Oulu

V. Ranasinghe, N. Rajatheva and M. Latva-aho, "Graph Neural Network Based Access Point Selection for Cell-Free Massive MIMO Systems," 2021 IEEE Global Communications Conference (GLOBECOM), Madrid, Spain, 2021, pp. 01-06, doi: 10.1109/GLOBECOM46510.2021.9685221

Graph neural network based access point selection for cell-free massive MIMO systems

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Author: Ranasinghe, Vismika1; Rajatheva, Nandana1; Latva-Aho, Matti1
Organizations: 1Centre for Wireless Communications, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.7 MB)
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Language: English
Published: IEEE, 2022
Publish Date: 2023-04-05


A graph neural network (GNN) based access point (AP) selection algorithm for cell-free mas-sive multiple-input multiple-output (MIMO) systems is proposed. Two graphs, a homogeneous graph which includes only AP nodes representing the structure of the APs in the network, and a heterogeneous graph which includes both AP nodes and user equipment (UE) nodes are constructed to represent a cell-free massive MIMO network. A GNN based on the inductive graph learning framework GraphSAGE is used to obtain the embed-dings which are then used to predict the links between the nodes. The numerical results show that compared to the proximity-based AP selection algorithms, the proposed GNN based algorithm predicts the potential APs with more accuracy. Compared to the large scale fading coefficient based AP selection algorithms, the proposed algorithm does not require measured and sorted signal strengths of all the neighbouring APs. Furthermore, the proposed algorithm is scalable in terms of the number of users in the cell-free system.

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ISBN: 978-1-7281-8104-2
ISBN Print: 978-1-7281-8105-9
Pages: 1 - 6
DOI: 10.1109/GLOBECOM46510.2021.9685221
Host publication: 2021 IEEE Global Communications Conference (GLOBECOM)
Conference: IEEE Global Communications Conference
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported by the Academy of Finland 6Genesis Flagship (grant no. 318927).
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
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