# An Adaptive Scheduling Mechanism Optimized for V2N Communications over Future Cellular Networks
###### Abstract
A more detailed description can be found in existing technical reports [5] that study system requirements and respective enabling technologies for ToD services using C-V2X networks. Regarding the HDM UC, vehicles share their sensor data with a High Definition (HD) map provider service, which builds and shares HD maps with other vehicles. The objective is to create precise and accurate, near real-time object positioning, which is dynamically updated in near real-time. Dynamic and accurate HD maps are essential building blocks for safe automated driving. The above-mentioned UCs are planned to be supported soon [6] and are used in this paper to investigate the technology readiness in supporting such critical applications, and therefore, the feasibility of actual deployment. The 5G New Radio (NR) access technology provides a flexible, scalable, and forward-compatible Physical (PHY) layer to support a wide range of center carrier frequencies, deployment options, and a variety of UCs. In relation to the allocation of radio resources, the selection of the scheduling algorithm greatly affects network performance, in terms of throughput, delay and PRR. As we present in the paper, the typical 5G scheduling schemes often cannot adequately support the strict requirements and fine differentiations in the quality of service (QoS) requirements of the V2X services, particularly when required to scale up in relation to the number of the vehicles in a dynamic and responsive-or even predictive-manner. Although scheduling is a well-researched topic, dedicated solutions, especially V2N communication, are still in their infancy. As discussed in our paper, existingsolutions do not consider the diversity of autonomous driving services with different data traffic characteristics and requirements. Thus, there are serious performance issues, especially on the UL when the network handles considerable user data traffic load. As it is presented in Section 2, well-established schedulers have been mainly developed to support enhanced Mobile Broadband (eMBB) communications. Moreover, solutions offered in the research literature do not always focus on V2N communications, several of these do not consider differential treatment for each UE session, or they use data traffic patterns and even deployment not specified by key standardization bodies like 3 GPP or experts in this area as 5 GAA. The purpose of the proposed scheme, called SOVANET (Scheduler Optimized for VANET), is to address this gap by providing a novel scheduler that considers well-defined autonomous driving services and provides a per-service solution to guarantee that critical services' resource demands are met and not overwhelmed by non-critical V2X services. Extensive simulations using the Network Simulator 3 (ns-3) [9] prove the efficiency of our proposed solution. More specifically, SOVANET is a novel scheduling mechanism that assesses the QoS requirements of each traffic flow for each vehicle (i.e., User Equipment-UE), using well-defined data traffic characteristics and allocates the available resources proportionally so that demanding services that are related to the safety of the passengers and vehicles will be prioritized in terms of allocated resources, and at the expense of non-safety demanding services. The main contributions of our work are as follows:  
* The implementation of the aforementioned scheduler. * The implementation of the scheduler using the ns-3 and the SUMO [10] environment calibrating the simulator based on 3 GPP's and 5 GAA data
* The performance comparison of the proposed scheduler against the Proportional Fair (PF) scheduler. As we will present, SOVANET considers the network load and handles critical and non-critical services for each UE separately by over-provisioning and under-provision resources accordingly. The results indicate that compared to the baseline scheduler, SOVANET provides promising results, especially on the UL or under highly loaded situations. The rest of the paper is organized as follows.