Resource Management in LADNs Supporting 5G V2X Communications

• Published in: IEEE access Vol. 11; pp. 63958 - 63971
• Main Authors: Hwang, Ren-Hung, Marzuk, Faysal, Sikora, Marek, Cholda, Piotr, Lin, Ying-Dar
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 5G mobile communication; Algorithms; Cloud computing; Energy consumption; Heuristic methods; Interference; local access data network (LADN); Network latency; Optimization; Quality of service; Quality of service architectures; Radio; Resource allocation; Resource management; Run time (computers); Signal to noise ratio; Uplinking; Vehicle-to-everything; vehicle-to-everything (V2X) communications; Vehicles
• DOI: 10.1109/ACCESS.2023.3288699
• ISSN: 2169-3536

Local access data networks (LADNs) are promising paradigms for reducing latency, decreasing energy consumption, and improving the quality of service (QoS) of fifth-generation (5G) radio access networks (RANs) that support vehicle-to-everything (V2X) communications. Remote radio heads (RRHs) that support V2X applications can be turned on or off depending on traffic demand to achieve optimal resource management and save energy by minimizing the activation of LADN servers in cloud-RANs (C-RANs). In this study, we investigated the problem of how to manage resources optimally in LADN while guaranteeing V2X QoS requirements. We formulated the resource allocation problem as an optimization problem to reduce the number of active RRHs subject to uplink bandwidth constraints. We calculated intercell interference (ICI) and uplink signal-to-interference-plus-noise ratio (SINR) to appropriately assign vehicles to RRHs. We solved the resource management problem by using an optimal algorithm and proposed heuristic algorithms to address the complexity of large-scale scenarios. The numerical results demonstrated that our model could efficiently utilize resources and provide optimal associations between vehicles and RRHs, thereby leading to energy savings. In particular, optimal associations could save up to 70% of energy in a scenario consisting of hundreds of vehicles. The computation time for a small-sized problem was approximately 60 ms, which means that the proposed model can be suitable for real-time control. Even on a large scale, the running time for a scenario with thousands of vehicles is still short. Therefore, the impact of vehicles' density is not harmful to the scalability of the whole approach.