Deep Reinforcement Learning-based Edge Discovery within the 3GPP Framework for C-ITS

초록

With the evolution of edge computing, addressing challenges within the framework of the Third Generation Partnership Project (3GPP) standard has garnered attention. In particular, challenges such as edge discovery and relocation, session management function (SMF) selection, and edge lifecycle management pose significant hurdles in providing seamless services, especially in advanced Cooperative Intelligent Transportation Systems (C-ITS). This paper proposes an intelligent solution for edge discovery tailored to continuously provision C-ITS services to users within the 3GPP framework. Leveraging deep reinforcement learning (DRL), our proposed algorithm facilitates optimal edge discovery based on specific user requirements. We demonstrate the compatibility of our approach with 3GPP standard operations and address the critical challenge of edge discovery by employing an intelligent DRL-based methodology.

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