Research on Predicting Alarm of Signaling Storm by Hybrid LSTM-AM Optimized With Improved PSO

Abstract

The prediction of the IP multimedia subsystem (IMS) signaling storm is crucial for ensuring the stable operation of voice over new radio (VoNR) services and enhancing operators' core competitiveness. However, the current IMS signaling storm prediction alarm function for live network systems lacks robustness, with most attention focused on equipment fault detection and network element health monitoring. To address this limitation, this paper proposes a signaling storm prediction model comprising two modules: prediction and judgment. The prediction module combines the advantages of long short-term memory (LSTM) models and an attention mechanism (AM), improving convergence and accuracy through an enhanced Particle Swarm Optimization (PSO) algorithm based on trigonometric transformation (TrigPSO). The judgment module effectively classifies predicted values into different alarm levels using K-Means. Experimental results based on data from China telecom's scientific apparatus show that the proposed model accurately predicts key indicator values, with an improved r-squared (R²) value of 0.854 compared to other models such as LSTM, LSTM-AM, LSTM-PSO, and LSTM-AM-PSO. Additionally, the k-means model performs well in experimental data validation, demonstrating its scientific validity and high efficiency.