URLLC场景下信道可靠连通度预测

第五代移动通信技术(5th generation,5G)中高可靠低时延通信(ultra reliability and low latency communication,URLLC)应用场景,十分契合航空5G机场场面宽带移动通信系统建设要求。以丢包率为定义的可靠性指标没有反应时变无线信道的时间依赖性和不同URLLC服务所需的持续时间。针对以上存在的问题,运用生存分析的方法,将URLLC关键技术与可靠性理论中失效率相结合,提出了可靠连通度指标,基于接收端信号强度,建立理论分布模型和数据驱动模型,对时变信道在未来1子帧内可靠连通度进行预测,并建立城市宏单元非视距簇时延线信道模型算例对模型进行对比分析,并在不同雨衰条件下,分析信道系统的可靠连通度。结果表明,数据驱动模型可靠性预测的均方误差(mean square error,MSE)可达0.1%,优于理论分布模型,且在恶劣天气情况下,多输入多输出信道可靠性相比于多输入单输出信道具有更高的抗衰落能力。

Effective Capacity of URLLC over Parallel Fading Channels with Imperfect Channel State Information

This paper investigates the effective capacity of a point-to-point ultra-reliable low latency communication(URLLC)transmission over multiple parallel sub-channels at finite blocklength(FBL)with imperfect channel state information(CSI).Based on reasonable assumptions and approximations,we derive the effective capacity as a function of the pilot length,decoding error probability,transmit power and the sub-channel number.Then we reveal significant impact of the above parameters on the effective capacity.A closed-form lower bound of the effective capacity is derived and an alternating optimization based algorithm is proposed to find the optimal pilot length and decoding error probability.Simulation results validate our theoretical analysis and show that the closedform lower bound is very tight.In addition,through the simulations of the optimized effective capacity,insights for pilot length and decoding error probability optimization are provided to evaluate the optimal parameters in realistic systems.

基于半静态调度与资源竞争的uRLLC业务时延分析

超可靠低时延通信(ultra Reliable and Low Latency Communication,uRLLC)是未来移动通信中的一种重要应用场景,是工业自动化、智能交通和远程医疗等众多新兴领域的技术基础。重点研究了无线通信系统中uRLLC业务的上行时延。建立了uRLLC业务的上行无线传输系统模型;从初传时延与单次重传时延2个角度分别进行问题建模与业务流时延分析,初传数据包采用半静态调度,重传数据包采用资源竞争调度;推导获得了包含多次重传的uRLLC业务的时延上界以及对应的概率分布。在理论分析的基础上,开发了基于NS3的uRLLC业务传输仿真平台,获得了uRLLC业务时延的仿真结果,并将其与时延上界理论计算结果对比,验证了时延上界理论分析的准确性。

eMBB和URLLC业务共存场景下的资源分配算法

研究增强移动宽带(Enhanced Mobile Broadband,eMBB)和超可靠低时延通信(Ultra-reliable Low-latency Communications,URLLC)的资源分配问题。给URLLC业务提供频谱接入的同时,为了减少对现有eMBB业务的干预,解决URLLC和eMBB业务之间的资源分配问题,在两者组成的无线系统中,提出一种罚函数算法,引入惩罚项将约束最优化问题转换为对一系列无约束最优化问题的求解。仿真实验结果表明,在满足URLLC业务时延和可靠性约束的前提下,提出算法能保持较高的eMBB业务数据传输速率。

智能电网邻域网中5G uRLLC的上行资源调度方案

智能电网利用新一代信息技术实现网络安全、可靠、高效地运行。智能电网邻域网(Smart Grid Neighborhood Area Network,SGNAN)负责处理终端发送到数据集中单元的数据,对数据传输有较高的实时性和可靠性要求。采用5G uRLLC(Ultra-reliable and Low Latency Communication)技术建立SGNAN的上行链路资源调度模型,并给出解决方案。该方案依据优先级动态分配资源,定义分配矩阵、速率矩阵表示系统吞吐量(目标函数),使用改进的人工蜂群算法求得系统的最优资源分配方案。实验结果表明,该方案能够有效保证终端实时性、公平性,并改善系统的吞吐量。

基于松弛时延的真空管高速飞车uRLLC贪心复用机制

真空管高速飞车的安全运行需要多种车地通信业务的有效支撑,包括超高可靠低时延通信(uRLLC)业务和增强移动带宽(eMBB)通信业务,异质业务资源共存时可通过临时抢占eMBB业务的时频域资源优先保障uRLLC业务的传输。根据误码率和时延指标将uRLLC业务划分为5个等级,并将uRLLC业务复用时延范围进行松弛,在考虑uRLLC业务误比特率和复用时频域范围约束条件下,构建以最小化uRLLC业务总功率为目标的优化问题;提出一种贪心策略,在指定步骤内得到次优解以应对高速变化的环境。仿真结果表明:在信道状态较差或噪声功率谱密度较高的情况下,本复用策略能显著降低uRLLC业务复用所消耗的功率,提升车地无线通信的可靠性。

基于微时隙的eMBB和uRLLC业务传输性能研究

超可靠低时延通信(Ultra Reliable Low Latency Communications, uRLLC)是5G移动通信技术的重要场景之一。相较于增强移动宽带(Enhanced Mobile Broadband, eMBB),uRLLC业务有严格的时延、可靠性要求,对数据速率的要求相对较低。在5G NR中,uRLLC业务可以使用微时隙结构抢占eMBB资源进行传输,保证uRLLC业务满足时延要求,同时实现与eMBB业务的复用。基于微时隙结构进行业务抢占的方法,研究了在微时隙结构部署uRLLC业务时,eMBB业务和uRLLC业务的传输性能。首先为eMBB业务用户配置传输资源,然后使用微时隙结构将uRLLC业务穿刺到正在运行的eMBB业务中,在uRLLC业务传输完成后的下一个时隙开展eMBB业务重传以保证uRLLC业务的低时延要求。为了进一步降低uRLLC业务的块误码率,在使用微时隙穿刺的同时,通过设置更大的子载波间隔抑制载波间干扰。通过理论分析评估了该场景中eMBB业务和uRLLC业务的块误码率(Block Error Rate, BLER)和吞吐量性能;在时延性能方面,在对用户到基站的空中接口时延分析的基础上,使用M/M/1队列模型等方法分析了用户接入基站的排队时延,进而得到基于微时隙的eMBB和uRLLC业务传输的总体时延。最后,通过仿真对上述性能分析进行了验证,仿真结果显示,与使用常规时隙传输相比,基于微时隙的eMBB和uRLLC业务传输在块误码率、空口时延和排队时延性能上有显著提升,可满足uRLLC业务的需求。

5G uRLLC应用场景分级及解决方案研究

为深入挖掘超高可靠、低时延的应用场景和需求,将uRLLC的能力最优匹配到行业应用中,从时延、可靠性、速率和移动性4个维度构建了“1+3”uRLLC无线场景分级体系,对低时延、高可靠各关键技术及产业成熟度进行研究分析,提出基于关键功能点组合的各场景分级的解决方案,并通过外场试点进行测试验证。

eMBB与URLLC资源分配专利技术综述

文章将eMBB与URLLC资源分配分为资源打孔/抢占、资源叠加、切片分配、联合资源分配等几个技术分支,简要分析该领域中各个申请人和国家的专利申请量以及相关专利申请涉及5G标准情况和被引证情况,从侧面反映了相关国家和申请人在该领域的发展状况和申请质量。在该领域发明专利申请实质审查中,根据不同技术分支的演进路线,有针对性地选择数据库进行检索,有效降低检索噪声、提高检索效率,对实际审查工作具有一定的指导意义。

5G URLLC应用场景中低时延、高可靠关键技术分析

文章在概述5G网络端到端传输时延类型及URLLC业务时延要求的基础上,对传输网URLLC关键技术和核心网URLLC关键技术展开分析,面向不同的URLLC业务应用场景,制定不同的技术组合方案,以优化5GURLLC网络应用,为运营商降低5G网络时延和提高可靠性提供技术参考。

面向5G URLLC业务的无线超低时延技术方案探讨

文章基于5G网络中URLLC业务所具备的低时延高可靠特性以及所带来的新的挑战,对空口时延的概念以及URLLC标准进展展开分析,并从减少传输时延间隔、优化资源调度方案等方面对5G无线超低时延技术方案和技术部署展开分析探讨,结果表明,为有效解决时延问题,3GPPR15标准中已经从优化资源调度和缩短传输时间间隔等方面提出可行思路,可为5G网络部署初期低时延业务提供技术支持。

Intelligent Preamble Allocation for Coexistence of mMTC/URLLC Devices:A Hierarchical Q-Learning Based Approach

The emergence of various commercial and industrial Internet of Things(IoT)devices has brought great convenience to people’s life and production.Both low-power,massively connected mMTC devices(MDs)and highly reliable,low-latency URLLC devices(UDs)play an important role in different application scenarios.However,when dense MDs and UDs periodically initiate random access(RA)to connect the base station and send data,due to the limited preamble resources,preamble collisions are likely to occur,resulting in device access failure and data transmission delay.At the same time,due to the highreliability demands of UDs,which require smooth access and fast data transmission,it is necessary to reduce the failure rate of their RA process.To this end,we propose an intelligent preamble allocation scheme,which uses hierarchical reinforcement learning to partition the UD exclusive preamble resource pool at the base station side and perform preamble selection within each RA slot at the device side.In particular,considering the limited processing capacity and energy of IoT devices,we adopt the lightweight Qlearning algorithm on the device side and design simple states and actions for them.Experimental results show that the proposed intelligent scheme can significantly reduce the transmission failure rate of UDs and improve the overall access success rate of devices.

Leveraging UAV-assisted communications to improve secrecy for URLLC in 6G systems

Unmanned Aerial Vehicles(UAVs)will be essential to support mission-critical applications of Ultra Reliable Low Latency Communication(URLLC)in futuristic Sixth-Generation(6G)networks.However,several security vulnerabilities and attacks have plagued previous generations of communication systems;thus,physical layer security,especially against eavesdroppers,is vital,especially for upcoming 6G networks.In this regard,UAVs have appeared as a winning candidate to mitigate security risks.In this paper,we leverage UAVs to propose two methods.The first method utilizes a UAV as Decode-and-Forward(DF)relay,whereas the second method utilizes a UAV as a jammer to mitigate eavesdropping attacks for URLLC between transmitter and receiver devices.Moreover,we present a low-complexity algorithm that outlines the two aforementioned methods of mitigating interception,i.e.increasing secrecy rate,and we compare them with the benchmark null method in which there is a direct communication link between transmitter and receiver without the UAV DF relay.Additionally,simulation results show the effectiveness of such methods by improving the secrecy rate and its dependency on UAV height,blocklength,decoding error probability and transmitter-receiver separation distance.Lastly,we recommend the best method to enhance the secrecy rate in the presence of an eavesdropper based on our simulations.

Optimizing Power Allocation for D2D Communication with URLLC under Rician Fading Channel:A Learning-to-Optimize Approach

To meet the high-performance requirements of fifth-generation(5G)and sixth-generation(6G)wireless networks,in particular,ultra-reliable and low-latency communication(URLLC)is considered to be one of the most important communication scenarios in a wireless network.In this paper,we consider the effects of the Rician fading channel on the performance of cooperative device-to-device(D2D)communication with URLLC.For better performance,we maximize and examine the system’s minimal rate of D2D communication.Due to the interference in D2D communication,the problem of maximizing the minimum rate becomes non-convex and difficult to solve.To solve this problem,a learning-to-optimize-based algorithm is proposed to find the optimal power allocation.The conventional branch and bound(BB)algorithm are used to learn the optimal pruning policy with supervised learning.Ensemble learning is used to train the multiple classifiers.To address the imbalanced problem,we used the supervised undersampling technique.Comparisons are made with the conventional BB algorithm and the heuristic algorithm.The outcome of the simulation demonstrates a notable performance improvement in power consumption.The proposed algorithm has significantly low computational complexity and runs faster as compared to the conventional BB algorithm and a heuristic algorithm.

Channel Coding at Finite Blocklength and Its Application in 6G URLLC

To support mission-critical applications, such as factory automation and autonomous driving, the ultra-reliable low latency communications (URLLC) is adopted in the fifth generation (5G) mobile communications network, which requires high level of reliability and low latency. Naturally, URLLC in the future 6G is expected to have a better capability than its 5G version which poses an unprecedented challenge to us. Fortunately, the potential solution can still be found in the well-known classical Shannon information theory. Since the latency constraint can be represented equivalently by blocklength, channel coding at finite blocklength plays an important role in the theoretic analysis of URLLC. Applying these achievements in rapidly development of massive MIMO techniques gives rise to a new theory on space time exchanging. It tells us that channel coding can also be performed in space domain, since it is capable of providing the same coding rate as that in time domain. This space time exchanging theory points out an exciting and feasible direction for us to further reduce latency in 6G URLLC. .

URLLC+工业互联网技术研究与业务应用

随着R16和R17版本冻结以及5G-A研究启动,为了利用高可靠低时延技术将时延、抖动和分组丢失率等控制在确定范围内,解决工业有线技术移动性差、组网不灵活和特殊环境铺设困难等问题,本文通过核心网控制面应急下沉技术提升5G专网的可靠性,规避园区外安全隐患;通过时频互补CA技术,实现上下行速率超千兆和极致端到端时延能力;通过高精度时间同步技术,实现电力差动保护测试误差时间为1.5μs左右,优于行业精度要求。推进5G技术与工业互联网深度融合,5G业务应用逐渐深入到工业核心生产环节。

A Collaborative Machine Learning Scheme for Traffic Allocation and Load Balancing for URLLC Service in 5G and Beyond

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. .

工业互联网场景下5G uRLLC资源调度研究

针对工业互联网场景下5G uRLLC资源调度问题,文章分析了工业互联网场景下uRLLC业务的超低时延传输、高可靠性以及低数据量的要求,着重分析超低时延传输与高可靠性2项指标,针对上行传输场景与下行链路场景分析资源调度目标,分别设计了基于Q学习算法的动态资源调度算法与基于终端业务调度时延要求的改进比例公平算法(DPF),实现工业互联网场景下5G uRLLC资源的合理调度。仿真分析结果表明,该调度方案在不同场景下均可以满足资源调度要求,上行传输场景的平均传输速率可达114.62 bps,下行链路场景可保证终端调度的优先级以及可靠性要求。

面向6G uRLLC的序号调制高可靠性传输技术研究

下一代移动通信网络将有效覆盖各类智能设备、工业物联网及车联网等应用,对不同应用中信息传输的可靠性、安全性及传输时延提出了更高的要求。本文提出一种基于载波序号调制(Orthogonal frequency division multiplexing with index modulation,OFDM-IM)的多分集传输方案,可有效提升高可靠低时延通信(ultra Reliable and Low Latency Communication,uRLLC)场景中信息传输的可靠性及系统调制解调的计算复杂度。为了降低接收端的计算复杂度,本文提出一种用于多分集OFDM-IM系统的基于单个子载波计算的接收机算法,通过将基于子块计算过程分解为两步计算的方式来降低解调过程的计算复杂度。计算机仿真结果证明了本文提出的多分集OFDM-IM可以获得比传统单个符号多次重发方式获得发送分集增益更优的误比特性能。

5G uRLLC能力需求及应用研究

为促进具备5G uRLLC特性的网络与终端设备产品的成熟,推动5G与行业应用的融合,梳理了5G uRLLC典型业务和需求,提出了3级uRLLC网络能力,研究并提出了通过能力提升集构建uRLLC网络能力。能力提升集包括构建低时延能力、高可靠能力、确定性能力、指标检测能力4个维度的端到端uRLLC关键技术,最后展示了5G-A uRLLC创新实践方案,以期为低延迟和高可靠性无线网络的设计提供一些演进思路。