Propagation Characteristics of 5G Electromagnetic Waves in Coal Mine Tunnels by Integrating SBR/IM and GA-SVM

With the advancement of intelligent construction in coal mines, 5G communication technology has become a key support for underground core networks. To analyze the electromagnetic wave propagation characteristics of 5G signals in coal mine tunnels, an innovative method that integrates the Shooting and Bouncing Ray/Image Method (SBR/IM) with a Genetic Algorithm-Optimized Support Vector Machine (GA-SVM) is proposed. The SBR/IM method is used to construct a three-dimensional electromagnetic simulation model of the tunnel, simulating multi-path propagation and generating high-precision training data. The GA-SVM algorithm is then employed to intelligently correct and predict signal path loss, enhancing model accuracy and generalization capability. Experimental results under typical tunnel conditions show that the proposed fusion model achieves a maximum path loss of only 63 dB at 0.2 m wall thickness, which is 51 dB lower than that of the traditional Floating Interception (FI) model. Moreover, the signal-to-noise ratio remains above 0 dB within 225 m, significantly outperforming the FI model. At a wall thickness of 0.3 m, the path loss of the fusion model is 72 dB. Additionally, the fusion model exhibits a received signal strength index of -68 dBm, a coherent bandwidth of 25 MHz, a throughput of 320 Mbps, and a delay as low as 15ms, all of which are superior to those of comparative models. The proposed hybrid method effectively enhances the propagation performance of 5G signals in coal mine tunnels, reduces path loss, improves signal quality, and provides a valuable approach for studying electromagnetic wave propagation in such environments.