Key challenges for 5G and Beyond networks relate with the requirements for exceptionally low latency, high reliability, and extremely high data rates. The Ultra-Reliable Low Latency Communication (URLLC) use case is t...Key challenges for 5G and Beyond networks relate with the requirements for exceptionally low latency, high reliability, and extremely high data rates. The Ultra-Reliable Low Latency Communication (URLLC) use case is the trickiest to support and current research is focused on physical or MAC layer solutions, while proposals focused on the network layer using Machine Learning (ML) and Artificial Intelligence (AI) algorithms running on base stations and User Equipment (UE) or Internet of Things (IoT) devices are in early stages. In this paper, we describe the operation rationale of the most recent relevant ML algorithms and techniques, and we propose and validate ML algorithms running on both cells (base stations/gNBs) and UEs or IoT devices to handle URLLC service control. One ML algorithm runs on base stations to evaluate latency demands and offload traffic in case of need, while another lightweight algorithm runs on UEs and IoT devices to rank cells with the best URLLC service in real-time to indicate the best one cell for a UE or IoT device to camp. We show that the interplay of these algorithms leads to good service control and eventually optimal load allocation, under slow load mobility. .展开更多
为更好地使URLLC(Ultra-Reliable and Low Latency Communications)与增强型移动宽带业务(eMBB:enhanced Mobile Broad Band)在同一载波频段有效复用,进一步提升混合业务系统性能,提出一种基于可扩展传输时间间隔(TTI:Transmission Time...为更好地使URLLC(Ultra-Reliable and Low Latency Communications)与增强型移动宽带业务(eMBB:enhanced Mobile Broad Band)在同一载波频段有效复用,进一步提升混合业务系统性能,提出一种基于可扩展传输时间间隔(TTI:Transmission Time Interval)的动态带宽分配策略。系统根据业务类型进行带宽动态划分;时域上提升URLLC调度优先级;频域上采用不同长度的TTI进行以用户为中心的无线资源分配。动态系统级仿真表明,在不同程度的负载水平下,相比传统无线资源分配算法,该方案能在有效满足URLLC用户时延需求的前提下优化eMBB用户的吞吐量消耗,URLLC用户时延增益最高达到83.8%,提升了5G混合业务系统中不同类型业务的服务质量(QoS:Quality of Service)。展开更多
文摘Key challenges for 5G and Beyond networks relate with the requirements for exceptionally low latency, high reliability, and extremely high data rates. The Ultra-Reliable Low Latency Communication (URLLC) use case is the trickiest to support and current research is focused on physical or MAC layer solutions, while proposals focused on the network layer using Machine Learning (ML) and Artificial Intelligence (AI) algorithms running on base stations and User Equipment (UE) or Internet of Things (IoT) devices are in early stages. In this paper, we describe the operation rationale of the most recent relevant ML algorithms and techniques, and we propose and validate ML algorithms running on both cells (base stations/gNBs) and UEs or IoT devices to handle URLLC service control. One ML algorithm runs on base stations to evaluate latency demands and offload traffic in case of need, while another lightweight algorithm runs on UEs and IoT devices to rank cells with the best URLLC service in real-time to indicate the best one cell for a UE or IoT device to camp. We show that the interplay of these algorithms leads to good service control and eventually optimal load allocation, under slow load mobility. .
文摘为更好地使URLLC(Ultra-Reliable and Low Latency Communications)与增强型移动宽带业务(eMBB:enhanced Mobile Broad Band)在同一载波频段有效复用,进一步提升混合业务系统性能,提出一种基于可扩展传输时间间隔(TTI:Transmission Time Interval)的动态带宽分配策略。系统根据业务类型进行带宽动态划分;时域上提升URLLC调度优先级;频域上采用不同长度的TTI进行以用户为中心的无线资源分配。动态系统级仿真表明,在不同程度的负载水平下,相比传统无线资源分配算法,该方案能在有效满足URLLC用户时延需求的前提下优化eMBB用户的吞吐量消耗,URLLC用户时延增益最高达到83.8%,提升了5G混合业务系统中不同类型业务的服务质量(QoS:Quality of Service)。