Distributed Edge Intelligence Enabled Wireless Communication Systems Serving Industrial Applications

Zusammenfassung

High requirements of industrial applications are challenging for WCSs, and advances in Artificial Intelligence (AI) bring a bright future in many fields. Hence, two directions from the aspects of WCS analysis and control are studied. Dependability assessment extracts dependability knowledge from given datasets, for which a series of AI methods are proposed, namely, deep autoencoder-based model, multi-task learning model, and device-level and system-level dependability assessment model. Spectral efficiency optimization aims to maximize spectral efficiency under certain conditions of quality of services. For this target, several AI models are proposed step by step for different conditions, namely, fully connected neural network, attention-based convolutional neural network, and generating precoder and power allocation neural network. Then, considering the resourceconstrained and distributed features, a distributed edge intelligence mechanism is proposed which can support AI applications on distributed edge devices. Evaluation on tasks of DA and SEO tasks indicates that the distributed edge intelligence has good executing efficiency.

Contents

Abbreviations vii

Zusammenfassung ix

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