V2X Meets NOMA: Non-Orthogonal Multiple Access for 5G Enabled Vehicular Networks

Benefited from the widely deployed infrastructure, the LTE network has recently been considered as a promising candidate to support the vehicle-to-everything (V2X) services. However, with a massive number of devices accessing the V2X network in the future, the conventional OFDM-based LTE network faces the congestion issues due to its low efficiency of orthogonal access, resulting in significant access delay and posing a great challenge especially to safety-critical applications. The non-orthogonal multiple access (NOMA) technique has been well recognized as an effective solution for the future 5G cellular networks to provide broadband communications and massive connectivity. In this article, we investigate the applicability of NOMA in supporting cellular V2X services to achieve low latency and high reliability. Starting with a basic V2X unicast system, a novel NOMA-based scheme is proposed to tackle the technical hurdles in designing high spectral efficient scheduling and resource allocation schemes in the ultra dense topology. We then extend it to a more general V2X broadcasting system. Other NOMA-based extended V2X applications and some open issues are also discussed.Comment: Accepted by IEEE Wireless Communications Magazin

Deep Reinforcement Learning for Resource Allocation in V2V Communications

In this article, we develop a decentralized resource allocation mechanism for vehicle-to-vehicle (V2V) communication systems based on deep reinforcement learning. Each V2V link is considered as an agent, making its own decisions to find optimal sub-band and power level for transmission. Since the proposed method is decentralized, the global information is not required for each agent to make its decisions, hence the transmission overhead is small. From the simulation results, each agent can learn how to satisfy the V2V constraints while minimizing the interference to vehicle-to-infrastructure (V2I) communications

Radio resource management for V2X in cellular systems

The thesis focuses on the provision of cellular vehicle-to-everything (V2X) communications, which have attracted great interest for 5G due to the potential of improving traffic safety and enabling new services related to intelligent transportation systems. These types of services have strict requirements on reliability, access availability, and end-to-end (E2E) latency. V2X requires advanced network management techniques that must be developed based on the characteristics of the networks and traffic requirements. The integration of the Sidelink (SL), which enables the direct communication between vehicles (i.e., vehicle-to-vehicle (V2V)) without passing through the base station into cellular networks is a promising solution for enhancing the performance of V2X in cellular systems. In this thesis, we addressed some of the challenges arising from the integration of V2V communication in cellular systems and validated the potential of this technology by providing appropriate resource management solutions. Our main contributions have been in the context of radio access network slicing, mode selection, and radio resource allocation mechanisms. With regard to the first research direction that focuses on the RAN slicing management, a novel strategy based on offline Q-learning and softmax decision-making has been proposed as an enhanced solution to determine the adequate split of resources between a slice for eMBB communications and a slice for V2X. Then, starting from the outcome of the off-line Q-learning algorithm, a low-complexity heuristic strategy has been proposed to achieve further improvements in the use of resources. The proposed solution has been compared against proportional and fixed reference schemes. The extensive performance assessment have revealed the ability of the proposed algorithms to improve network performance compared to the reference schemes, especially in terms of resource utilization, throughput, latency and outage probability. Regarding the second research direction that focuses on the mode selection, two different mode selection solutions referred to as MSSB and MS-RBRS strategies have been proposed for V2V communication over a cellular network. The MSSB strategy decides when it is appropriate to use one or the other mode, i.e. sidelink or cellular, for the involved vehicles, taking into account the quality of the links between V2V users, the available resources, and the network traffic load situation. Moreover, the MS-RBRS strategy not only selects the appropriate mode of operation but also decides efficiently the amount of resources needed by V2V links in each mode and allows reusing RBs between different SL users while guaranteeing the minimum signal to interference requirements. The conducted simulations have revealed that the MS-RBRS and MSSB strategies are beneficial in terms of throughput, radio resource utilization, outage probability and latency under different offered loads comparing to the reference scheme. Last, we have focused on the resource allocation problem including jointly mode selection and radio resource scheduling. For the mode selection, a novel mode selection has been presented to decide when it is appropriate to select sidelink mode and use a distributed approach for radio resource allocation or cellular mode and use a centralized radio resource allocation. It takes into account three aspects: the quality of the links between V2V users, the available resources, and the latency. As for the radio resource allocation, the proposed approach includes a distributed radio resource allocation for sidelink mode and a centralized radio resource allocation for cellular mode. The proposed strategy supports dynamic assignments by allowing transmission over mini-slots. A simulation-based analysis has shown that the proposed strategies improved the network performance in terms of latency of V2V services, packet success rate and resource utilization under different network loads.La tesis se centra en la provisión de comunicaciones para vehículos sistemas celulares (V2X: Vehicle to Everything), que han atraído un gran interés en el contexto de 5G debido a su potencial de mejorar la seguridad del tráfico y habilitar nuevos servicios relacionados con los sistemas inteligentes de transporte. Estos tipos de servicios tienen requisitos estrictos en términos fiabilidad, disponibilidad de acceso y latencia de extremo a extremo (E2E). Para ello, V2X requiere técnicas avanzadas de gestión de red que deben desarrollarse en función de las características de las redes y los requisitos de tráfico. La integración del Sidelink (SL), que permite la comunicación directa entre vehículos (es decir, vehículo a vehículo (V2V)) sin pasar por la estación base de las redes celulares, es una solución prometedora para mejorar el rendimiento de V2X en el sistema celular. En esta tesis, abordamos algunos de los desafíos derivados de la integración de la comunicación V2V en los sistemas celulares y validamos el potencial de esta tecnología al proporcionar soluciones de gestión de recursos adecuadas. Nuestras principales contribuciones han sido en el contexto del denominado "slicing" de redes de acceso radio, la selección de modo y los mecanismos de asignación de recursos radio. Respecto a la primera dirección de investigación que se centra en la gestión del RAN slicing, se ha propuesto una estrategia novedosa basada en Q-learning y toma de decisiones softmax como una solución para determinar la división adecuada de recursos entre un slice para comunicaciones eMBB y un slice para V2X. Luego, a partir del resultado del algoritmo de Q-learning, se ha propuesto una estrategia heurística de baja complejidad para lograr mejoras adicionales en el uso de los recursos. La solución propuesta se ha comparado con esquemas de referencia proporcionales y fijos. La evaluación ha revelado la capacidad de los algoritmos propuestos para mejorar el rendimiento de la red en comparación con los esquemas de referencia, especialmente en términos de utilización de recursos, rendimiento, y latencia . Con respecto a la segunda dirección de investigación que se centra en la selección de modo, se han propuesto dos soluciones de diferentes llamadas estrategias MSSB y MS-RBRS para la comunicación V2V a través de una red celular. La estrategia MSSB decide cuándo es apropiado usar el modo SL o el modo celular, para los vehículos involucrados, teniendo en cuenta la calidad de los enlaces entre los usuarios de V2V, los recursos disponibles y la situación de carga de tráfico de la red. Además, la estrategia MS-RBRS no solo selecciona el modo de operación apropiado, sino que también decide eficientemente la cantidad de recursos que los enlaces V2V necesitan en cada modo, y permite que los RB se reutilicen entre diferentes usuarios de SL al tiempo que garantiza requisitos mínimos de señal a interferencia. Se ha presentado un análisis basado en simulación para evaluar el desempeño de las estrategias propuestas. Finalmente, nos hemos centrado en el problema conjunto de la selección de modo y la asignación de recursos de radio. Para la selección de modo, se ha presentado una nueva estrategia para decidir cuándo es apropiado seleccionar el modo SL y usar un enfoque distribuido para la asignación de recursos de radio o el modo celular y usar la asignación de recursos de radio centralizada. Tiene en cuenta tres aspectos: la calidad de los enlaces entre los usuarios de V2V, los recursos disponibles y la latencia. En términos de asignación de recursos de radio, el enfoque propuesto incluye una asignación de recursos de radio distribuida para el modo SL y una asignación de recursos de radio centralizada para el modo celular. La estrategia propuesta admite asignaciones dinámicas al permitir la transmisión a través de mini-slots. Los resultados muestran las mejoras en términos de latencia, tasa de recepción y la utilización de recursos bajo diferentes cargas de red.Postprint (published version

D2D-based V2V Communications with Latency and Reliability Constraints

Direct device-to-device (D2D) communication has been proposed as a possible enabler for vehicle-to-vehicle (V2V) applications, where the incurred intra-cell interference and the stringent latency and reliability requirements are challenging issues. In this paper, we investigate the radio resource management problem for D2D-based V2V communications. Firstly, we analyze and mathematically model the actual requirements for vehicular communications and traditional cellular links. Secondly, we propose a problem formulation to fulfill these requirements, and then a Separate Resource Block allocation and Power control (SRBP) algorithm to solve this problem. Finally, simulations are presented to illustrate the improved performance of the proposed SRBP scheme compared to some other existing methods

Ultra-Reliable Low-Latency Vehicular Networks: Taming the Age of Information Tail

While the notion of age of information (AoI) has recently emerged as an important concept for analyzing ultra-reliable low-latency communications (URLLC), the majority of the existing works have focused on the average AoI measure. However, an average AoI based design falls short in properly characterizing the performance of URLLC systems as it cannot account for extreme events that occur with very low probabilities. In contrast, in this paper, the main objective is to go beyond the traditional notion of average AoI by characterizing and optimizing a URLLC system while capturing the AoI tail distribution. In particular, the problem of vehicles' power minimization while ensuring stringent latency and reliability constraints in terms of probabilistic AoI is studied. To this end, a novel and efficient mapping between both AoI and queue length distributions is proposed. Subsequently, extreme value theory (EVT) and Lyapunov optimization techniques are adopted to formulate and solve the problem. Simulation results shows a nearly two-fold improvement in terms of shortening the tail of the AoI distribution compared to a baseline whose design is based on the maximum queue length among vehicles, when the number of vehicular user equipment (VUE) pairs is 80. The results also show that this performance gain increases significantly as the number of VUE pairs increases.Comment: Accepted in IEEE GLOBECOM 2018 with 7 pages, 6 figure