Low-Cost Learning-Based Path Loss Estimation Using Correlation Graph CNN
論文ID: 2022EAL2094
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論文ID: 2022EAL2094
In this paper, we propose a method for predicting radio wave propagation using a correlation graph convolutional neural network (C-Graph CNN). We examine what kind of parameters are suitable to be used as system parameters in C-Graph CNN. Performance of the proposed method is evaluated by the path loss estimation accuracy and the computational cost through simulation.
