Intelligent 6G Admission Control Leveraging LSTM-based Request Forecasting

5G mechanisms typically rely on resource over-provisioning or static reservations to satisfy the stringent demands of critical connections. Consequently, 5G networks are inefficient in fulfilling application's low delay and high reliability requirements. Emerging 6G use cases will be even more demanding in terms of delay and reliability. Therefore, the usage of existing mechanisms would result in immense operational costs for the mobile network operators due to their in-efficiency. This highlights the need for investigating sophisticated mechanisms with the evolution towards 6G networks, providing reliability in a cost-efficient manner. This paper examines how to prioritize critical connections over non-critical ones, while constructively exploiting the available resources. To achieve this goal, we propose an intelligent admission control (AC) scheme for a radio access node (AN). More specifically, using AI/ML techniques, the AN forecasts the number of incoming critical connections and supports their reliability requirements by dynamically reserving resources for them, consequently affecting the admission of non-critical connections. Our simulation-based evaluations show that the proposed approach improves a baseline approach - not using intelligence - as it is capable of providing reliability to a larger number of connections, while efficiently utilizing system's resources. Thus, our work provides evidence of the potential of using intelligence for next-generation admission control design.