Radio Propagation Graph Representation Learning: An Implementation in Multi-Hop Path Representation

This paper proposes a novel model for radio propagation graph representation learning in multi-hop path representation. A concept of radio propagation graph representation learning has been proposed to express the radio propagation process using graph data structure, thereby improving the prediction accuracy with small computation time. In the existing work, a direct path representation model based on feedforward neural network (FFNN) has been proposed; however, it achieves poor prediction accuracy at points far from a transmitter due to the lack of consideration of reflection, i.e., multi-hop path. Therefore, this paper proposes a novel learning method to apply the existing concept to multi-hop path cases. Through the performance evaluation using Long Term Evolution signal data in a 2120 MHz band measured in an urban area, we demonstrate that the proposed model outperforms the existing representation model in terms of prediction accuracy and achieves shorter computation time compared with the ray tracing application.