窄带物联网非连续接收机制功耗模型与优化

doi:10.11959/j.issn.1000-436x.2019094

通信学报 ›› 2019, Vol. 40 ›› Issue (4): 107-116.doi: 10.11959/j.issn.1000-436x.2019094

窄带物联网非连续接收机制功耗模型与优化

简鑫,韦一笑,刘钰芩,宋健,曾孝平,谭晓衡

重庆大学微电子与通信工程学院，重庆 400044

修回日期:2019-03-20 出版日期:2019-04-25 发布日期:2019-05-05
作者简介:高简鑫（1987- ），男，四川自贡人，博士，重庆大学副教授、硕士生导师，主要研究方向为应用统计数学、物联网无线接入技术及应用等。博|韦一笑（1993- ），女，广西河池人，重庆大学硕士生，主要研究方向为窄带物联网、物联网超低功耗技术等。|刘钰芩（1991- ），女，四川南充人，重庆大学硕士生，主要研究方向为窄带物联网、群智感知技术等。|宋健（1995- ），男，江西宜春人，重庆大学硕士生，主要研究方向为智慧网络、群体智能等。|曾孝平（1956- ），男，四川广安人，博士，重庆大学教授、博士生导师，主要研究方向为航空移动通信、下一代移动通信、生物信号处理等。|谭晓衡（1976- ），男，重庆人，博士，重庆大学教授、博士生导师，主要研究方向为下一代移动通信、通信信号处理等。
基金资助:
国家自然科学基金资助项目(61501065);国家自然科学基金资助项目(61601067);国家自然科学基金资助项目(61571069);重庆市基础科学与前沿技术研究专项基金资助项目(cstc2016jcyjA0021);中央高校基本科研业务费基金资助项目(2018CDXYTX0009)

Power consumption modeling and optimization for NB-IoT eDRX

Xin JIAN,Yixiao WEI,Yuqin LIU,Jian SONG,Xiaoping ZENG,Xiaoheng TAN

College of Micro-Electronics and Communication Engineering,Chongqing University,Chongqing 400044,China

Revised:2019-03-20 Online:2019-04-25 Published:2019-05-05
Supported by:
The National Natural Science Foundation of China(61501065);The National Natural Science Foundation of China(61601067);The National Natural Science Foundation of China(61571069);Chongqing Research Program of Basic Research and Frontier Technology(cstc2016jcyjA0021);The Fundamental Research Funds for the Central Universities(2018CDXYTX0009)

摘要/Abstract

摘要：

针对窄带物联网（NB-IoT）的 3 种典型业务场景（周期自动报告、异常自动报告和软件升级/重配置业务），为NB-IoT扩展型非连续接收（eDRX）机制建立了以终端工作状态为状态变量的马尔可夫模型，并给出相应的功耗与时延模型。其中，连接态被分为随机接入态和数据收发态，以细化因海量用户并发入网产生碰撞而带来的额外能耗。鉴于周期自动报告（MAR-P）业务的发生频次最高，重点分析了MAR-P业务激励时NB-IoT eDRX机制的电池使用寿命及其优化设计方案。数值表明，传输周期、最大随机接入次数、最大数据重传次数和系统负载直接决定电池使用寿命，上述参数合理配置可有效延长电池使用寿命。这为 NB-IoT 终端行为级建模与优化设计提供了参考。

关键词: 窄带物联网, 扩展型非连续接收, 马尔可夫链, 功耗模型

Abstract:

To extend the battery life of massive machine type devices (MTD),narrow-band Internet of things (NB-IoT) system extended discontinuous reception (eDRX) mechanism of LTE to the maximum as well as introduced a lower power state named as power saving mode (PSM).Three Markov models were established for three typical NB-IoT traffic scenarios,which called mobile autonomous reporting periodic report (MAR-P),mobile autonomous reporting exception report (MAR-E),software update/reconfiguration (SUR).The states of each Markov model were the working status of MTDs,including connected,idle and PSM state,in which the connected state was divided into random access state and data translating/receiving state to further evaluate the additional power consumption caused by collisions from massive MTDs concurrent access.Thereby,the power consumption and delay models with respect to each traffic scenarios were derived.Since the frequency of MAR-P traffic was far greater than the other two,the battery life of this traffic case with its optimal design choice was comprehensively analyzed.Numerical results show that,the battery life is mostly influenced by transmission period,maximum number of random access attempts,maximum number of data transmissions and traffic load,which can be maximized by appropriate parameters setting up.These works provide good references for NB-IoT device behavior modeling and optimization design.

Key words: narrow-band Internet of things, extended discontinuous reception, Markov chain

中图分类号:

TN929.5

简鑫,韦一笑,刘钰芩,宋健,曾孝平,谭晓衡. 窄带物联网非连续接收机制功耗模型与优化[J]. 通信学报, 2019, 40(4): 107-116.

Xin JIAN,Yixiao WEI,Yuqin LIU,Jian SONG,Xiaoping ZENG,Xiaoheng TAN. Power consumption modeling and optimization for NB-IoT eDRX[J]. Journal on Communications, 2019, 40(4): 107-116.

图/表 11

图1

图2

表1

图3

图4

表2

参数设置"

参数	取值
E/Wh	5
W₁/mW	0.02
W₄/mW	100
E_RACH、E_TR/J	0.05
$\frac{1}{λ_{r}} 、 \frac{1}{λ_{t}} / m s$	5
T_r、T_ARQ/ms	8
T_i/ms	64

表2

图5

图6

图7

图8

表3

Rmax、Nmax优化结果"

序号	参数取值					优化结果
序号	$\frac{m}{n}$	p_t	T_p/h	P_RACH,set	P_TR,set	$R_{m a x}^{*}$	$N_{m a x}^{*}$	L*/年	D*/s	$P_{s u c}^{*}$
1	0.5	0.1	0.1	0.95	0.95	4	2	8.02	12.57	96.63%
2	0.5	0.1	0.1	0.6	0.6	1	1	13.02	113.39	76.07%
3	0.5	0.1	0.5	0.95	0.95	4	2	18.88	62.84	96.63%
4	0.5	0.1	1	0.95	0.95	4	2	22.73	125.67	96.63%
5	1	0.5	0.1	0.95	0.95	7	5	1.99	27.25	92.97%
6	1	0.5	0.1	0.5	0.5	1	1	8.58	1 597.18	18.39%
7	1	0.5	0.5	0.95	0.95	7	5	7.78	136.17	92.97%
8	1	0.5	0.5	0.75	0.75	4	2	10.44	1 056.01	63.03%
9	1	0.5	1	0.95	0.95	7	5	12.24	272.31	92.97%
10	2	0.8	0.1	0.95	0.95	21	14	0.22	35.37	91.09%
11	2	0.8	0.1	0.13	0.2	1	1	6.88	12 944.00	2.707%
12	2	0.8	0.5	0.95	0.95	21	14	1.07	176.21	91.09%
13	2	0.8	0.5	0.25	0.35	2	2	10.35	180 11.00	9.085%
14	2	0.8	1	0.95	0.95	21	14	2.07	352.26	91.09%
15	2	0.8	1	0.35	0.35	3	2	11.60	6.86	12.73%

表3

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业务类别	传输数据类型	数据规模/B	发生时间间隔
周期自动报告	定期上传信息	20～200	1 d（40%）、2 h（40%）、1 h（15%）、30 min（5%）
异常自动报告	异常突发信息	20～200	几个月甚至几年
软件升级/重配置	软件升级补丁	200～2 000	约180 d

窄带物联网非连续接收机制功耗模型与优化

Power consumption modeling and optimization for NB-IoT eDRX

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