An Analytical Latency Model and Evaluation of the Capacity of 5G NR to Support V2X Services Using V2N2V Communications

5G has been designed to support applications such as connected and automated driving. To this aim, 5G includes a highly flexible New Radio (NR) interface that can be configured to utilize different subcarrier spacings (SCS), slot durations, scheduling, and retransmissions mechanisms. This flexibility can be exploited to support advanced V2X services with strict latency and reliability requirements using V2N2V (Vehicle-to-Network-to-Vehicles) communications instead of direct or sidelink V2V (Vehicle-to-Vehicle). To analyze this possibility, this paper presents a novel analytical model that estimates the latency of 5G at the radio network level. The model accounts for the use of different numerologies (SCS, slot durations and Cyclic Prefixes), modulation and coding schemes, full-slots or mini-slots, semi-static and dynamic scheduling, different retransmission mechanisms, and broadcast/multicast or unicast transmissions. The model has been used to first analyze the impact of different 5G NR radio configurations on the latency. We then identify which radio configurations and scenarios can 5G NR satisfy the latency and reliability requirements of V2X services using V2N2V communications. This paper considers cooperative lane changes as a case study. The results show that 5G can support advanced V2X services at the radio network level using V2N2V communications under certain conditions that depend on the radio configuration, bandwidth, service requirements and cell traffic load

End-to-End V2X Latency Modeling and Analysis in 5G Networks

networks provide higher flexibility and improved performance compared to previous cellular technologies. This has raised expectations on the possibility to support advanced Vehicle to Everything (V2X) services using the cellular network via Vehicle-to-Network (V2N) and Vehicle-to-Network-to-Vehicle (V2N2V) connections. The possibility to support critical V2X services using 5G V2N2V or V2N connections depends on their end-to-end (E2E) latency. The E2E latency of V2N2V or V2N connections depends on the particular 5G network deployment, dimensioning and configuration, in addition to the network load. To date, few studies have analyzed the capabilities of V2N2V or V2N connections to support critical V2X services, and most of them focus on the 5G radio access network or consider dedicated 5G pilot deployments under controlled conditions. This paper progresses the state-of-the-art by introducing a novel E2E latency model to quantify the latency of 5G V2N and V2N2V communications. The model includes the latency introduced at the radio, transport, core, Internet, peering points and application server (AS) when vehicles are supported by a single mobile network operator (MNO) and when they are supported by multiple MNOs. The model can quantify the latency experienced when the V2X AS is deployed from the edge of the network (using MEC platforms) to the cloud. Using this model, this study estimates the E2E latency of 5G V2N2V connections for a large variety of possible 5G network deployments and configurations. The analysis helps identify which 5G network deployments and configurations are more suitable to meet V2X latency requirements. To this aim, we consider as case study the cooperative lane change service. The conducted analysis highlights the challenge for centralized network deployments that locate the V2X AS at the cloud to meet the latency requirements of advanced V2X services. Locating the V2X AS closer to the cell edge reduces the latency. However, it requires a higher number of ASs and also a careful dimensioning of the network and its configuration to ensure sufficient network and AS resources are dedicated to serve the V2X traffic

Trajectory optimization of autonomous vehicles considering radio access network constraints

The aim of this project is to combine the research work done so far on vehicle navigation and mobile data networks for vehicle-to-network applications. With this, autonomous vehicles would be able to choose a route from predefined starting and destination points, that assure to them the minimum data rate requirements across the route for the target level of autonomy in the driving. For this we propose two algorithms. The first one is an autonomous car-oriented algorithm that aims at maximizing the number of Autonomous Vehicles that can travel at the same time. The second one is an operator-oriented algorithm that allows the network to maintain as much as possible communication resources at the Base Stations that would be ready to use for other mobile services. A comparison between both algorithms is also presented in order to determine which algorithm would be a better fit for each use case of autonomous driving.El objetivo de este proyecto es combinar la investigación hecha hasta ahora en navegación de vehículos y la investigación en redes de datos móviles para aplicaciones vehicle-to-network. Con esto, los vehículos autónomos podrían escoger una ruta, a partir de un origen y un destino predefinidos, que les asegurase la mínima tasa de datos requerida para el nivel de autonomía de la conducción requerido. Para esto proponemos dos algoritmos. El primero es un algoritmo orientado al coche autónomo que pretende maximizar el número de coches autónomos que pueden viajar al mismo tiempo. El segundo es un algoritmo orientado al operador que permite a la red mantener en la medida de lo posible recursos de comunicaciones en las estaciones base que pueden ser usados por otros servicios móviles. Una comparación entre los dos algoritmos es presentada para determinar que algoritmo resulta mejor en cada caso específico de conducción autónoma.L'objectiu d'aquest projecte és combinar la recerca feta fins ara en navegació en vehicles i les xarxes de dades mòbils per a aplicacions vehicle-to-network. Amb això, els vehicles autònoms series capaços d'escollir una ruta a partir d'uns punts d'inici i fi predefinits que els assegurés la mínima velocitat de dades a través de tota la ruta per un nivell d'autonomia vehicular específic. Per això proposem dos algoritmes. El primer és un algoritme orientat al cotxe autònom que pretén maximitzar el número de vehicles autònoms que poden circular al mateix temps. El segon és un algoritme orientat a l'operador que permet a la xarxa mantenir, en la mesura del possible, recursos de comunicacions a les estacions base que podrien ser utilitzats per altres serveis mòbils. Una comparació entre els dos algoritmes també es presenta per tal de determinar quin algoritme és millor car a cada cas específic de conducció autònoma