This study investigates radio-frequency signal propagation in underground metro tunnels with a focus on distributed antenna system (DAS) deployment. Deterministic simulations were performed using Altair WinProp 2024.1 (ProMan) with a 3D ray-tracing engine (GO + UTD) at 2.4 GHz in a reinforced concrete tunnel model of 900 m length. Two antenna configurations (B3: 8 dBi directional; B8: 5 dBi wide-beam) were evaluated under identical geometric and material conditions. Results show that path loss varies from 42 to 65 dB over 850 m, with estimated attenuation exponents lower than free-space values due to quasi-waveguide effects. The B3 configuration provides higher near-field received power (up to −7.5 dBm) but exhibits stronger attenuation over long distances. In contrast, the B8 configuration ensures a more uniform spatial power distribution and a reduced path-loss growth rate beyond 500 m. The findings confirm that antenna radiation pattern significantly influences underground communication performance and demonstrate the engineering suitability of distributed antenna systems for stable metro tunnel coverage.

This paper presents a physics-informed simulation study on improving Distributed Antenna Systems (DAS) performance in underground mining environments using Artificial Intelligence (AI). The simulation models dust, humidity, and metallic structures, which were found to degrade communication by reducing signal stability by 15–20%, adding up to 12 dB of attenuation at humidity above 70%, and increasing interference by 30–35%. An AI-based adaptive control pipeline was implemented, combining multilevel learning for power and tilt control, meta-learning for rapid adaptation to changing conditions, and adaptive beamforming for interference suppression. In mining-like conditions, this pipeline increased effective coverage from 72.0% to 90.0%, reduced adaptation time from 2.00 s to 1.64 s, and lowered packet loss from 10.0% to 8.0%. The models achieved a mean squared error below 0.03 and prediction accuracy up to 92%, indicating that AI-driven DAS control can improve underground communication reliability by 20–25% and has potential to reduce production-related costs by about 30%.

This paper presents the results of a study aimed at improving the performance of Distributed Antenna Systems in underground mining environments through the implementation of an adaptive control algorithm. Thanks to the integration of various devices and systems, the possibility of intelligent control of the antenna infrastructure has emerged, which allows optimizing the operation of communication networks and increasing their performance. The Python programming environment was used for modeling. Simulation results demonstrated a 30% increase in signal quality and a 40% reduction in communication interruptions during emergency scenarios. The adaptive algorithm dynamically responds to environmental changes in real time, significantly enhancing system reliability. The results were validated by comparing them with experimental data, showing an accuracy within a 5% error margin. However, the implementation of adaptive algorithms also led to a 20–30% increase in operational costs.