A Novel Method for Susceptibility Threshold Prediction in Communication Systems via Multitask Learning

Wireless communication systems have become the cornerstone of modern society. However, the escalating issue of electromagnetic interference poses a significant challenge to the quality and stability of communications. This article aims to assess and analyze the electromagnetic susceptibility of wireless communication systems for enhancing communication performance. This article proposes an advanced predictive model, multifaceted interference susceptibility threshold prediction (MIST-P) via enhanced multitask learning, which integrates multitask learning (MTL) and data augmentation to accurately predict the susceptibility threshold of communication systems in complex electromagnetic environments. By assessing the preformance of four different single task networks, this article adopts the best-performing deep neural network with Xavier initialization as the basis for MTL framework. The MIST-P model is capable of concurrently learning the system's susceptibility under various interference signals and capturing cross-task common features through a shared underlying network structure. This approach improves the generalization ability and prediction accuracy, keeping the prediction error within 0.24 dB. The measured data shows that the MIST-P model not only conserves computational resources, but also holds theoretical and practical implications for the management and optimization of interference in communication systems, providing an effective strategy for the design of future communication systems.