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

With the evolution of edge computing, addressing challenges within the framework of the Third Generation Part-nership Project (3GPP) standard has garnered attention. In particular, challenges such as edge discovery and relocation, session management function (SMF) selection, and edge lifecycle man-agement 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 3G PP 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 cmplovina an intelliaent DRL-based methodology,