Interference monitoring technology for mine-used 5G communication signal transmission

Compared to conventional interference suppression techniques, such as adaptive filtering and adaptive interference cancellation, blind source separation (BSS) offers the advantage of separating multiple mixed signals with lower computational complexity and higher robustness. However, BSS has limitations in addressing the complex and dynamic interference sources found in underground mining environments. It also lacks automated mechanisms for analyzing and evaluating processed signal components, which not only hampers communication efficiency but may also lead to safety risks due to residual interference. To overcome these challenges, this study proposed a neural network-based interference monitoring and suppression method tailored for mine-used 5G communication signal transmission. By analyzing the characteristics of interference sources in key areas such as main haulage roadways, fully mechanized mining faces, and substations, the study identified the suppression and mitigation of spike interference and crosstalk signals as critical for 5G anti-interference performance. The proposed method initially employed BSS for the preliminary separation of interference components in mine-used 5G communication signals. It then leveraged a neural network for feature extraction and deep analysis of the separated signals, enabling precise identification and quantification of residual interference. If the monitored interference signal exceeded a preset threshold, the system automatically triggered a new round of suppression, forming an iterative and optimized closed-loop control process. Experimental results revealed that: In a 100 MHz full-bandwidth transmission environment, the proposed method achieved a suppression gain of 13 dB for both spike interference and crosstalk signals, showing improvements of approximately 117% and 86%, respectively, over BSS-based interference suppression alone. Compared to traditional techniques, such as BSS, the proposed method enhanced the signal-to-noise ratio (SNR) by an average of 15.56% and reduced the bit error rate (BER) by an average of 21.88%, which could significantly improve signal quality.