Microwave Link Failures Prediction via LSTM-based Feature Fusion Network

Microwave links are widely employed in cellular data networks due to high-speed Internet access and easy installation, thus reducing network implementation costs. However, these links are prone to failure and may lead to performance degradation, unavailability and service disruption. Early detection of any link failures is critical to maintain network quality, but the complex environment and the dynamic nature of link information makes this a complicated process. In this work, we propose a Long Short-Term Memory (LSTM)-based feature fusion network (LSTM-FFN) to fuse and encode both homophy and structural equivalence relationships in the LSTM temporal feature learning network. This will simultaneously model the spatial and temporal features exhibited in Long-Term Evolution (LTE) networks to detect any link failures. Our proposed method effectively avoids the gradient exploding problem that RNN-based STGNN faced. This multi-scale topological feature fusion allows the LSTM-FFN to further explore the spatial dependencies among node/link and include additional structural equivalence in modeling compared with previous network failure detection work. The evaluation results show that LSTM-FFN outperforms other statistical-based methods with and without network topology encoded, and reaches 94.1% precision, 90.2% recall and 92.1% f1-score.