URLLC场景下信道可靠连通度预测

doi:10.12305/j.issn.1001-506X.2024.05.34

摘要/Abstract

摘要：

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。

关键词: 5G通信, 高可靠低时延通信场景, 可靠性, 机器学习

Abstract:

The application scenario of ultra reliability and low latency communication (URLLC) in the 5th generation mobile communication technology (5G) is very suitable for the construction of aeronautical mobile airport communications system in the 5G airport scene. The reliability indicator defined by packet loss rate does not reflect the time dependence of time-varying wireless channels and the transmission duration required by different URLLC services. In view of the above problems, the survival analysis method is adopted, the key technology of URLLC with the failure rate in reliability theory is combined, and the reliable connectivity indicator is proposed, which is based on the receiver signal strength. A theoretical distribution model and a data-driven model are proposed to predict the reliable connectivity of time-varying channels in the next subframe, and an example of the urban macro-nonLine of sight-cluster time delay line channel model is established to compare and analyze the models, and the reliable connectivity of the channel system is analyzed under different rain decline conditions. The results show that the data-driven model reliability prediction has a mean square error (MSE) of up to 0.1%, which is better than the theoretical distribution model, and the multi-input multi-output channel reliability has higher fading resistance compared to the multi-input single-output channel under severe weather conditions.

Key words: 5G communication, ultra reliability and low latency communication (URLLC) scenario, reliability, machine learning

中图分类号:

TN914

王希, 任惠, 王威, 张嘉怡, 赵洪山. URLLC场景下信道可靠连通度预测[J]. 系统工程与电子技术, 2024, 46(5): 1810-1819.

Xi WANG, Hui REN, Wei WANG, Jiayi ZHANG, Hongshan ZHAO. Channel connectivity reliability prediction in URLLC scenario[J]. Systems Engineering and Electronics, 2024, 46(5): 1810-1819.

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参数名称	参数设置
大尺度衰落场景	UMa-NLOS
基站功率/dBm	36
水平收发距离d_2D/km	1.8
基站高度h_BS/m	23
终端高度h_UT/m	1.5
有效环境高度h_E/m	1
叶面损耗/dB	15
雨衰系数k	0.232 2
雨衰系数α	0.923 1
Tx天线阵列	[2, 2, 2, 1, 1]
Rx天线阵列	[1, 1, 2, 1, 1]
发射机数量	8
接收机数量	2
时延配置	“CDL-B”
采样频率/Hz	4×103
载波频率/GHz	30
带宽B/MHz	100
噪声系数NF/dB	6.72

样本量	接收机	参数	估计值
1 000	RX1	形状参数ξ	0.027 16
	RX1	尺度参数σ	1.015 40
	RX2	形状参数ξ	0.025 69
	RX2	尺度参数σ	1.064 36

类型	LOG-MSE/dB	预测值	实际值	误差
LSTM	RX1:-54.712 7	99.76	99.81	0.05
LSTM	RX2:-54.066 8	99.76	99.81	0.05
BPNN	RX1:-53.809 1	99.82	99.81	0.01
BPNN	RX2:-54.078 2	99.82	99.81	0.01
威布尔	RX1:14.455 5	99.88	99.81	0.07
威布尔	RX2:14.174 3	99.88	99.81	0.07

降雨等级	降水量/(mm/h)	雨衰/dB	阈值R₀/dB
小雨	< 0.416 7	0.103 5	-7.560 9
中雨	0.416 7~1.041 7	0.241 1	-7.423 3
大雨	1.041 7~2.083 3	0.427 2	-7.237 2
暴雨	2.083 3~4.167 7	0.867 1	-6.797 3
大暴雨	4.167 7~10.416 7	2.019 7	-5.644 7

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