AI/ML Based Blockage Prediction Using Beam Measurement Reports for mmWave V2X Communication Systems

Le résumé fourni par la source

This paper presents a blockage prediction system based on artificial intelligence/machine learning (AI/ML) for millimeter wave (mmWave) communications, with a particular focus on vehicular scenarios. Although numerous related works on ML-based blockage prediction, these works focus on simple scenarios, where the trajectory of the moving object is either fixed or deterministic. In order to make the blockage prediction system more practical, this paper assumes a nondeterministic situation such as both communication terminals and blockers are moving. Furthermore, we have formulated a practical AI/ML-based blockage prediction procedure that is operable in 3GPP compliant systems with taking its beam measurement reports and frame structure. The proposed prediction method uses the measurement report for beam management as input of a neural network. The computer simulation is conducted to show prediction performance of proposed method.

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