Emerging 5G wireless technology will support services and use cases with vastly heterogeneous requirements. Network slicing, which allows composing multiple dedicated logical networks with specific functionality running on top of a common infrastructure, is introduced as a solution to cope with this heterogeneity. At the radio access network (RAN), the use of network slicing involves the assignment of radio resources to each slice in accordance with its expected requirements and functionalities. Therefore, RAN slicing will provide the required design flexibility and will be necessary for any network slicing solution. This paper investigates the RAN slicing problem for providing two generic services of 5G, namely enhanced mobile broadband (eMBB) and vehicle-to-everything (V2X). In this respect, we propose an efficient RAN slicing scheme based on an off-line reinforcement learning followed by a low-complexity heuristic algorithm, which allocates radio resources to different slices with the target of maximizing the resource utilization while ensuring the availability of resources to fulfill the requirements of the traffic of each RAN slice. A simulation-based analysis is presented to assess the performance of the proposed solution. The simulation results have shown that the proposed algorithm improves the network performance in terms of resource utilization, the latency of V2X services, achievable data rate, and outage probability. INDEX TERMS Vehicle-to-everything (V2X), reinforcement learning, network slicing, RAN slicing.

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Recently, vehicle-to-vehicle (V2V) communications for future cellular systems have attracted considerable attention due to their potential benefits such as improved system capacity, spectral efficiency or delay reduction, and they enable new services related to intelligent transportation systems. V2V communications can be performed using two communication modes, namely cellular mode, based on uplink/downlink communications, and sidelink mode, which enables vehicles to communicate directly with other vehicles. However, selecting the appropriate operating mode according to the requirements of V2V services and allocating the radio resources to V2V communications becomes a challenging issue. In this respect, we propose a novel mode selection and resource reuse strategy to select the appropriate mode of operation and decide the amount of resource blocks (RBs) for V2V in cellular and sidelink modes, where multiple V2V links may share the same radio resources. A simulation-based analysis is presented to assess the performance of the proposed solution. The simulation results have shown that the proposed strategy improves network performance in terms of achievable throughput, outage probability, latency, and resource utilization.

A widely deployed cellular network, supported by direct connections, can offer a promising solution that supports new services with strict requirements on access availability, reliability, and end-to-end (E2E) latency. The communications between vehicles can be made using different radio interfaces: One for cellular communication (i.e., cellular communication over the cellular network based on uplink (UL)/downlink (DL) connections) and the other for direct communication (i.e., D2D-based direct communications between vehicles which allows vehicular users (V-UEs) to communicate directly with others). Common cellular systems with licensed spectrum backed by direct communication using unlicensed spectrum can ensure high quality of service requirements for new intelligent transportation systems (ITS) services, increase network capacity and reduce overall delays. However, selecting a convenient radio interface and allocating radio resources to users according to the quality of service (QoS) requirements becomes a challenge. In this regard, let’s introduce a new radio resource allocation strategy to determine when it’s appropriate to establish the communication between the vehicles over a cellular network using licensed spectrum resources or D2D-based direct connections over unlicensed spectrum sharing with Wi-Fi. The proposed strategy aims at meeting the quality of service requirements of users, including reducing the possibility of exceeding the maximum delay restrictions and enhancing network capacity utilization in order to avoid service interruption. The proposed solution is evaluated by highlighting different conditions for the considered scenario, and it is demonstrated that the proposed strategy improves network performance in terms of transmitted data rate, packet success rate, latency, and resource usage

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