Anticipatory Slice Resource Reservation for 5G Vehicular URLLC Based on Radio Statistics

In this paper, we consider resource allocation for vehicular safety traffic. In 5G, this traffic is carried by using the Ultra-Reliable and Low-Latency Communications (URLLC) service as it needs stringent Quality of Service (QoS) requirements in terms of latency and reliability. Since URLLC services may require specific numerology and/or channel access and retransmission strategies, network slicing has been proposed as a solution for QoS requirements and its coexistence with other services such as enhanced Mobile Broad-Band (eMBB). In order to accommodate URLLC traffic, one can opt for static resource reservation, however this is not optimal as it does not follow the real URLLC traffic present in the cell and can impact negatively eMBB traffic. Reactive, on-demand resource reservation is not feasible either as it requires reconfiguration which introduces extra delay that makes it prohibitive to meet URLLC delay requirements. This paper proposes proactive resource reservation schemes that anticipate slice demand. Resource reservation is computed per gNodeB based on the expected traffic and radio conditions. We show how field measurements and trajectory predictions can be used to achieve URLLC objectives with low impact on eMBB performance.