计算机科学 ›› 2021, Vol. 48 ›› Issue (5): 283-288.doi: 10.11896/jsjkx.200300019

• 计算机网络 • 上一篇    下一篇

一种H2H和M2M混合场景下的前导码资源动态分配机制

王聪1, 魏成强2, 李宁2, 马文峰1, 田辉1   

  1. 1 陆军工程大学野战工程学院 南京210007
    2 陆军工程大学通信工程学院 南京210007
  • 收稿日期:2020-03-03 修回日期:2020-06-30 出版日期:2021-05-15 发布日期:2021-05-09
  • 通讯作者: 王聪(lgd_dolphin@139.com)
  • 基金资助:
    国家自然科学基金(61771486);江苏省博士后科研资助计划项目(2019K090)

Dynamic Allocation Mechanism of Preamble Resources Under H2H and M2M Coexistence Scenarios

WANG Cong1, WEI Cheng-qiang2, LI Ning2, MA Wen-feng1, TIAN Hui1   

  1. 1 College of Field Engineering,Army Engineering University of PLA,Nanjing 210007,China
    2 College of Communications Engineering,Army Engineering University of PLA,Nanjing 210007,China
  • Received:2020-03-03 Revised:2020-06-30 Online:2021-05-15 Published:2021-05-09
  • About author:WANG Cong,born in 1975,Ph.D,associate professor.His main researchinte-rests include mobile communications,M2M communications and computer networks.
  • Supported by:
    National Natural Science Foundation of China(61771486) and Jiangsu Planned Projects for Postdoctoral Research Funds(2019K090).

摘要: 在H2H和M2M混合场景下,随着大规模M2M(Machine-to-Machine)设备接入网络,受限于有限的接入前导码资源,H2H(Human-to-Human)用户的接入成功率会大幅降低。针对此问题,提出了一种H2H和M2M混合场景下的前导码资源动态分配机制。在满足H2H平均接入时延要求的情况下,动态调整分配给M2M设备的前导码资源数量,然后根据分配的资源数量,自适应调整每个随机接入子帧内参与竞争的M2M设备数量,最大化M2M设备的接入效率。对M2M设备接入成功率及H2H平均接入时延进行仿真,结果表明所提方法相比固定资源分配机制,在H2H平均访问时延较低的情况下,明显提高了M2M设备的接入成功率。

关键词: ACB机制, M2M通信, 资源分配, 组寻呼

Abstract: As massive M2M devices are connected to the network,the network performance declines sharply.At the same time,due to limited preamble resources,the access success probability of H2H users is severely reduced.To solve this problem,this paper proposes a dynamic allocation mechanism of preamble resources in hybrid H2H and M2M scenarios.In this method,on the condition that the H2H average access delay meets requirements,the amount of preamble resources allocated to M2M devices is dynamically adjusted.Then,according to the amount of allocated preamble resources,the number of M2M devices competing in each random-access opportunity is dynamically adjusted to maximize the access efficiency of M2M devices.Through the simulation of the success probability of M2M devices and the average H2H access delay,experimental results show that this method significantly improves the success probability of M2M devices when the average H2H access delay is low,compared with the fixed resource allocation mechanism.

Key words: ACB mechanism, Group paging, M2M communication, Resource allocation

中图分类号: 

  • TN929.5
[1]HUSSAIN F,ANPALAGAN A,VANNITHAMBY R.Medium access control techniques in M2M Communication:Survey and Critical Review [J].Transactions on Emerging Telecommunications Technologies,2014,28(1):1-24.
[2]LI S S,XU L D,ZHAO S S.5G Internet of Things:A Survey[J].Journal of Industrial Information Integration,2018,10:1-9.
[3]3GPP.3GPP RAN2 71:Pull Based RAN Overload Control[R].Madrid,Spain:3GPP,2010.
[4]3GPP.3GPP TR 23.898:Access Class Barring and Overload Protection (Release 7)[R].3GPP,2005.
[5]WEI C H,CHENG R G,TSAO S L.Performance Analysis of Group Paging for Machine-Type Communications in LTE Networks[J].IEEE Transactions on Vehicular Technology,2013,62(77):3371-3382.
[6]CHEN J,LIN Y,CHENG R.A Delayed Random Access Speed-up Scheme for Group Paging in Machine-Type Communications[C]//Proceedings of the 2015 IEEE International Conference on Communications.Taipei,Taiwan,2015:623-627.
[7]SUI N N,XU Y Y,WANG C,et al.Performance Analysis of a Novel Hybrid s-Aloha/Tdma Protocol for Beta Distributed Massive MTC Access[J].Sensors,2017,17(12):1-25.
[8]HARWAHYU R,CHENG R G,SARI R F.Consecutive Group Paging for LTE Networks Supporting Machine-type Communications Services[C]//Proc.IEEE 24th Int.Symp.Pers.Indoor Mobile Radio Commun.(PIMRC).2013:1619-1623.
[9]HARWAHYU R,WANG X,SARI R,et al.Analysis of Group Paging with Pre-Backoff[J].EURASIP Journal on Wireless Communications & Networking,2015,2015(1):34.
[10]CHENG R G,FIRAS A T,CHEN J H,et al.A Dynamic Resource Allocation Scheme for Group Paging in LTE-Advanced Networks[J].IEEE Internet of Things Journal,2015,2(5):427-434.
[11]KSENTINI A,HADJADJ-AOU Y,TALEB T.Cellular-basedmachine-to-machine:Overload control[J].IEEE Network,2012,26(6):54-60.
[12]HE H,DU Q,SONG H.Traffic-aware ACB Scheme for Massive Access in Machine-to-Machine Networks[C]//Proceedings of the 2015 IEEE International Conference on Communications (ICC).2015:617-622.
[13]DUAN S,SHAH-MANSOURI V,WONG V W S.Dynamic Access Class Barring for M2M Communications in LTE Networks[C]//Proceedings of the 2013 IEEE Globecom Workshops (GC Wkshps).2013:9-13.
[14]DUAN S,SHAH-MANSOURI V,WANG Z.D-acb:AdaptiveCongestion Control Algorithm for Bursty M2M Traffic in LTE Networks[J].IEEE Transactions on Vehicular Technology,2016,65(12):9847-9861.
[15]ZANGAR N,GHARBI S,ABDENNEBI M.Service Differentiation Strategy based on MACB Factor for M2M Communications in LTE-A Networks[C]//Proceedings of the 2016 13th IEEE Consumer Communications & Networking Conference.2016:9-12.
[16]3GPP.3GPP TR 37.868:Study on RAN Improvements for Machine-Type Communications[R].Sophia-Antipolis Cedex,France:.3GPP,2011.
[17]MALAK D,HUANG H,ANDREWS G.Throughput maximization for delay-sensitive random-access communication[J].IEEE Transactions on Wireless Communications,2019,18(1):709-723.
[18]CHENG R G,HUAN Y S,HAWAHYU R.Two-phase random-access procedure for LTE-A networks[J].IEEE Transactions on Wireless Communications,2019,18(4):2374-2387.
[19]PARK E,BAE J,HAN Y.Energy-efficient random access for LTE-based stationary IoT networks[J].IEEE Communication Letters,2019,23(2):346-349.
[20]KIM T,BANG I.An enhanced random access with preamble-assisted short-packet transmissions for cellular IoT communications[J].IEEE Communication Letter,2019,23(6):1081-1084.
[21]OH C Y,HWANG D,LEE T J.Joint Access Control and Resource Allocation for Concurrent and Massive Access of M2M Devices[J].IEEE Transactions on Wireless Communications,2015,14(8):4182-4192.
[22]LI N,CAO C,WANG C.Dynamic Resource Allocation and Access Class Barring Scheme for Delay-Sensitive Devices in Machine to Machine (M2M) Communications[J].Sensors,2017,17(6):1-20.
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