Computer Networks ( IF 5.6 ) Pub Date : 2021-08-30 , DOI: 10.1016/j.comnet.2021.108415 Shavbo Salehi 1 , Behdis Eslamnour 1
In the past few years, the continual improvement of 5G technology has supported powerful IoT (Internet of Things) and IIoT (Industrial IoT) devices which have been used to provide a wide range of services. One of the service types that has been noticed recently is URLLC (Ultra-Reliable Low-Latency Communication) service that requires highly reliable communication and low latency bounds. In this paper, intending to meet the latency and reliability requirements of IIoT users located outdoor in a hard-to-reach area, we propose to use a UAV-BS (Unmanned Aerial Vehicle Base Station) for air-to-ground (A2G) communications. Despite the very fact that UAV-BSs have been used widely, yet they have some shortcomings in flight time. So we proposed an energy-efficient trajectory design method to reduce the UAV-BS’s energy consumption for both the communication and mobility functions while fulfilling the application’s reliability and latency requirements. We proposed a UMC-IRSA (UAV-BS Multi-Channel Irregular Repetition Slotted-ALOHA) method to adapt the New Radio (NR) distinct frame structure for IIoT users. The IIoT nodes are clustered by the UMC-IRSA method (based on Mahalanobis distance) to decrease the UAV-BS energy consumption. The simulation results show that the UMC-IRSA clustering method combined with the Q-Learning algorithm for clusters serving decreases the UAV-BS energy consumption for flying in fixed altitude. The reduction in energy consumption provided by using the combination of UMC-IRSA and Q-Learning, in comparison to the combination of UMC-IRSA and Random Serving, combination of CRP (Chinese Restaurant Process) and Q-Learning, and CRP and Random Serving is 19%, 24%, and 31% respectively.
中文翻译:
通过不规则重复时隙-ALOHA提高工业物联网URLLC服务的无人机基站能效
在过去几年中,5G 技术的不断改进支持了强大的 IoT(物联网)和 IIoT(工业物联网)设备,这些设备已用于提供广泛的服务。最近注意到的一种服务类型是 URLLC(超可靠低延迟通信)服务,它需要高度可靠的通信和低延迟界限。在本文中,为了满足位于室外难以到达区域的 IIoT 用户的延迟和可靠性要求,我们建议使用 UAV-BS(无人机基站)进行空对地 (A2G)通讯。尽管UAV-BS已经被广泛使用,但它们在飞行时间上存在一些不足。因此,我们提出了一种节能的轨迹设计方法,以降低 UAV-BS 在通信和移动功能方面的能耗,同时满足应用程序的可靠性和延迟要求。我们提出了一种 UMC-IRSA(UAV-BS 多通道不规则重复时隙-ALOHA)方法来适应 IIoT 用户的新无线电(NR)独特的帧结构。IIoT 节点通过 UMC-IRSA 方法(基于马哈拉诺比斯距离)进行聚类,以降低 UAV-BS 能耗。仿真结果表明,UMC-IRSA聚类方法与Q-Learning算法相结合的聚类服务降低了UAV-BS定高飞行的能耗。通过使用组合提供的能源消耗减少 我们提出了一种 UMC-IRSA(UAV-BS 多通道不规则重复时隙-ALOHA)方法来适应 IIoT 用户的新无线电(NR)独特的帧结构。IIoT 节点通过 UMC-IRSA 方法(基于马哈拉诺比斯距离)进行聚类,以降低 UAV-BS 能耗。仿真结果表明,UMC-IRSA聚类方法与Q-Learning算法相结合的聚类服务降低了UAV-BS定高飞行的能耗。通过使用组合提供的能源消耗减少 我们提出了一种 UMC-IRSA(UAV-BS 多通道不规则重复时隙-ALOHA)方法来适应 IIoT 用户的新无线电(NR)独特的帧结构。IIoT 节点通过 UMC-IRSA 方法(基于马哈拉诺比斯距离)进行聚类,以降低 UAV-BS 能耗。仿真结果表明,UMC-IRSA聚类方法与Q-Learning算法相结合的聚类服务降低了UAV-BS定高飞行的能耗。通过使用组合提供的能源消耗减少 仿真结果表明,UMC-IRSA聚类方法与Q-Learning算法相结合的聚类服务降低了UAV-BS定高飞行的能耗。通过使用组合提供的能源消耗减少 仿真结果表明,UMC-IRSA聚类方法结合Q-Learning算法进行集群服务,降低了UAV-BS定高飞行的能耗。通过使用组合提供的能源消耗减少UMC-IRSA和Q-Learning,对比UMC-IRSA和Random Serving的组合,CRP(Chinese Restaurant Process)和Q-Learning的组合,CRP和Random Serving分别是19%、24%和31% .