Graph Neural Network Enabled Propagation Graph Method For Channel Modeling

Document Type

Article

Source of Publication

Ieee Transactions On Vehicular Technology

Publication Date

9-1-2024

Abstract

Channel modeling is considered as a fundamental step in the design, deployment, and optimization of vehicular wireless communication systems. For typical vehicular communication scenarios in urban areas, dense multipath may exist in the wireless channels. The propagation graph (PG) method is an efficient approach to simulate multipath radio propagation. In this paper, we extend the PG method into a Graph Neural Network (GNN) enabled data-driven method for calculating channel transfer function (CTF) and channel impulse response (CIR) in a given space. ChebNet, a classical GNN, is utilized for estimating the scattering coefficients of the edge gains in the PG method. The proposed GNN-enabled method performs better than baseline algorithms, such as multilayer perceptron (MLP), simulated annealing (SA) algorithm, and genetic algorithm (GA) in effectively estimating a large number of scattering coefficients in PG. Mean absolute errors of the proposed method are provided and evaluated in this paper. Additionally, the potential future research directions of the GNN-enabled PG method for channel modeling are discussed.

ISSN

0018-9545

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Volume

73

Issue

9

First Page

12280

Last Page

12289

Disciplines

Electrical and Computer Engineering

Keywords

Wireless communication, Scattering, Channel estimation, Data models, Graph neural networks, Genetic algorithms, Channel impulse response, Channel modeling, graph neural network (GNN), propagation graph (PG), ray-tracing (RT)

Indexed in Scopus

no

Open Access

no

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