Resource Management in LADNs Supporting 5G V2X Communications

被引:0
|
作者
Hwang, Ren-Hung [1 ]
Marzuk, Faysal [2 ]
Sikora, Marek [2 ]
Cholda, Piotr [2 ]
Lin, Ying-Dar [3 ]
机构
[1] Natl Yang Ming Chiao Tung Univ, Coll Artificial Intelligence, Tainan 711, Taiwan
[2] AGH Univ Krakow, Inst Telecommun, PL-30059 Krakow, Poland
[3] Natl Yang Ming Chiao Tung Univ, Dept Comp Sci, Hsinchu 300, Taiwan
关键词
Vehicle-to-everything; Resource management; 5G mobile communication; Optimization; Interference; Signal to noise ratio; Quality of service; 5G; local access data network (LADN); optimization; resource management; vehicle-to-everything (V2X) communications; ALLOCATION; NETWORKS;
D O I
10.1109/ACCESS.2023.3288699
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:63958 / 63971
页数:14
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