Intelligent Admission Control in 6G Networks for Resource-efficient Reliable Connectivity.

Recent studies on designing 6G networks, especially when it comes to satisfying the stringent latency and reliability requirements, identify native integration of Artificial Intelligence (AI) as a key enabler. Although 5G systems support features to provide high-reliability communication, they mostly rely on resource over-provisioning and are hence inefficient. We identify the need for addressing the problem of resource-efficiency from the perspective of 6G systems. The dynamic behaviors of radio access networks, due to varying radio channel conditions, pose an additional challenge in achieving high resource-efficiency. In this respect, we present and evaluate a Machine Learning (ML)-based mechanism for efficient support of safety-critical communication with stringent latency and reliability requirements. This mechanism is embedded in the admission control (AC) of an access node (AN) and uses Least Absolute Shrinkage and Selection Operator (LASSO)-based resource budget prediction while admitting new connection requests to the network. The goal is to maximize the number of clients admitted into the system while maintaining the reliability of the previously admitted critical connections. Our simulation-based evaluations highlight that the proposed approach outperforms the baseline approach - not featuring ML - by about 17% in terms of the average per-session reliability of safety-critical connections at different network load conditions, and hence, provide further evidence on the potentials of ML for next-generation networks design.