Computer Science ›› 2020, Vol. 47 ›› Issue (4): 243-248.doi: 10.11896/jsjkx.190300410

• Computer Network • Previous Articles     Next Articles

Non-orthogonal Random Access Resource Allocation Scheme Based on Terminal Grouping

ZHANG Ji-rong, JIA Chen-qing   

  1. School of Communication and Information Engineering,Xi’an University of Posts and Telecommunications,Xi’an 710000,China
  • Received:2019-03-15 Online:2020-04-15 Published:2020-04-15
  • Contact: JIA Chen-qing,born in 1993,postgra-duate.Her main research interests include access devices and communication network.
  • About author:ZHANG Ji-rong,born in 1963,Ph.D,professor.Her main research interests include switches,access devices and broadband communication networks.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China(61871321),National Science and Technology Major Project (2016ZX03001016) and Innovation Team Project of Shaanxi Province (2017KCT-30-02)

Abstract: In order to solve the collisions,resource shortages and other problems in Machine to Machine(M2M) communication,a non-orthogonal random access and data transmission scheme based on terminal Grouping was proposed,i.e.TG-NORA-DT scheme.Firstly,the machine type communication devices (MTCDs) are grouped according to the speed of energy consumption,and the priority of the group is set.Secondly,the difference of arrival time is used to identify multiple MTCDs with the same preambles,and the power reuse of conflicting MTCDs is realized in the subsequent access process.Finally,based on the TG-NORA-DT scheme,a resource allocation method is proposed to reasonably allocate resources between the physical random access channel (PRACH) and the physical uplink shared channel (PUSCH).Simulation results show that compared with orthogonal random access and data transmission protocol(ORADTP) and Non-Orthogonal Random Access-Data Transmission(NORA-DT) scheme,TG-NORA-DT scheme improves system throughput and resource efficiency,and decreases the probability of preamble collisions.The resourse efficiency has increased by more than 20%.

Key words: M2M communication, Non-orthogonal random access, Resource allocation, Resource efficiency, Terminal grouping

CLC Number: 

  • TN929
[1]JUNAID M,SHAH M A,SATTI I A.A survey of internet of things,enabling technologies and protocols[C]//2017 23rd International Conference on Automation and Computing (ICAC).Huddersfield:IEEE Press,2017:1-5.
[2]ZHAO G F,CHEN J,HAN Y B,et al.Prospective networktechniques for 5G mobile communication:A survey[J].Journal of Chongqing University of Posts and Telecommunications(Nature Science Edition),2015,27(4):441-452.
[3]XIA N,CHEN H,YANG C,et al.Radio Resource Management in Machine-to-Machine Communications-A Survey[J].IEEE Communications Surveys & Tutorials,2018,20(1):791-828.
[4]MA Z F,CHAI R,ZHOU Y.Utility function maximizationbased joint cell selection and power allocation for heterogeneous M2M communication networks[C]//2018 27th Wireless and Optical Communication Conference (WOCC).Hualien:IEEE Press,2018:1-6.
[5]WANG G.Algorithm Study on M2M Communication Random Access[D].Beijing:Tsinghua University,2011.
[6]TR 37.868 V11.0.0.Study on RAN Improvements for Machine-type Communications[S].3GPP,2011.
[7]WANG Z H,WONG W S.Optimal Access Class Barring forStationary Machine Type Communication Devices With Timing Advance Information[J].IEEE Transactions on Wireless Communications,2015,14(10):5374-5387.
[8]GAO E C.Research on Access and Load Balancing Technology for IoT Ultra-dense Scenarios[D].Xi’an:Xidian University,2018.
[9]ZHAN W,DAI L.Throughput optimization for massive random access of M2M communications in LTE networks[C]//2017 IEEE International Conference on Communications (ICC).Pa-ris:IEEE Press,2017:1-6.
[10]LIANG Y N,LI X,ZHANG J Y,et al.Non-Orthogonal Random Access for 5G Networks[J].IEEE Transactions on Wireless Communications,2017,16(7):4817-4831.
[11]WIRIAATMADJA D T,CHOI K W.Hybrid Random Accessand Data Transmission Protocol for Machine-to-Machine Communications in Cellular Networks[J].IEEE Transactions on Wireless Communications,2015,14(1):33-46.
[12]BAI J,LI Y,GUO X.Resource Allocation in Non-OrthogonalRandom Access for M2M Communications[C]//2018 IEEE 87th Vehicular Technology Conference (VTC Spring).Porto:IEEE Press,2018:1-5.
[13]HSU C,REN Y,LIN K C,et al.Hey! I Have Something forYou:Paging Cycle Based Random Access for LTE-A[C]//2018 IEEE International Conference on Communications (ICC).Kansas City:IEEE Press,2018:1-6.
[14]LIU D X,WANG C,LI N,et al.Novel Clustered Access Method for Improving Access Probability of Massive M2M Communications[J].Communications Technology,2017,50(8):1683-1690.
[15]YANG W J,LI C Q,ZHANG J R.D2D communication mothod and resource allocation algorithm in 5G networks[J].Journal of Xi’an University of Posts and Telecommunications,2017,22(4):26-30.
[16]LU Y,GUAN G M,ZHU X R.A Group-based Random Access Mechanism for M2M Communications[J].Study on Optical Communications,2017(5):65-69.
[17]SHI Y Y,LU G Y.Optimized random access mechanism forM2M service in LTE system[J].Application Research Compu-ters,2013,30(12):3742-3744.
[18]LI W Y,DU Q H,LIU L J,et al.Dynamic Allocation of RACH Resource for Clustered M2M Communications in LTE Networks[C]//2015 International Conference on Identification,Information,and Knowledge in the Internet of Things (IIKI).Beijing:IEEE Press,2015:140-145.
[19]SHI C Z,ZHAO H G,LI X.Energy Group based Random Access Method for M2M Communication[C]//2018 IEEE Internation Conference on Signal Processing,Communications and Computing(ICSPCC).Qingdao:IEEE Press,2018:1-5.
[20]TR36.213 V12.4.0.Evolved Universal Terrestrial Radio Access (E-UTRA);physical Layer Producedures[S].3GPP,2014.
[1] YU Bin, LI Xue-hua, PAN Chun-yu, LI Na. Edge-Cloud Collaborative Resource Allocation Algorithm Based on Deep Reinforcement Learning [J]. Computer Science, 2022, 49(7): 248-253.
[2] TANG Feng, FENG Xiang, YU Hui-qun. Multi-task Cooperative Optimization Algorithm Based on Adaptive Knowledge Transfer andResource Allocation [J]. Computer Science, 2022, 49(7): 254-262.
[3] LI Meng-fei, MAO Ying-chi, TU Zi-jian, WANG Xuan, XU Shu-fang. Server-reliability Task Offloading Strategy Based on Deep Deterministic Policy Gradient [J]. Computer Science, 2022, 49(7): 271-279.
[4] ZHOU Tian-qing, YUE Ya-li. Multi-Task and Multi-Step Computation Offloading in Ultra-dense IoT Networks [J]. Computer Science, 2022, 49(6): 12-18.
[5] QIU Xu, BIAN Hao-bu, WU Ming-xiao, ZHU Xiao-rong. Study on Task Offloading Algorithm for Internet of Vehicles on Highway Based on 5G MillimeterWave Communication [J]. Computer Science, 2022, 49(6): 25-31.
[6] XU Hao, CAO Gui-jun, YAN Lu, LI Ke, WANG Zhen-hong. Wireless Resource Allocation Algorithm with High Reliability and Low Delay for Railway Container [J]. Computer Science, 2022, 49(6): 39-43.
[7] SHEN Jia-fang, QIAN Li-ping, YANG Chao. Non-orthogonal Multiple Access and Multi-dimension Resource Optimization in EH Relay NB-IoT Networks [J]. Computer Science, 2022, 49(5): 279-286.
[8] PAN Yan-na, FENG Xiang, YU Hui-qun. Competitive-Cooperative Coevolution for Large Scale Optimization with Computation Resource Allocation Pool [J]. Computer Science, 2022, 49(2): 182-190.
[9] WANG Ying-kai, WANG Qing-shan. Reinforcement Learning Based Energy Allocation Strategy for Multi-access Wireless Communications with Energy Harvesting [J]. Computer Science, 2021, 48(7): 333-339.
[10] SONG Hai-ning, JIAO Jian, LIU Yong. Research on Mobile Edge Computing in Expressway [J]. Computer Science, 2021, 48(6A): 383-386.
[11] WANG Cong, WEI Cheng-qiang, LI Ning, MA Wen-feng, TIAN Hui. Dynamic Allocation Mechanism of Preamble Resources Under H2H and M2M Coexistence Scenarios [J]. Computer Science, 2021, 48(5): 283-288.
[12] LI Zhen-jiang, ZHANG Xing-lin. Resource Allocation and Offloading Decision of Edge Computing for Reducing Core Network Congestion [J]. Computer Science, 2021, 48(3): 281-288.
[13] XU Xu, QIAN Li-ping, WU Yuan. Computation Resource Allocation and Revenue Sharing Based on Mobile Edge Computing for Blockchain [J]. Computer Science, 2021, 48(11): 124-132.
[14] LIU Tong, FANG Lu, GAO Hong-hao. Survey of Task Offloading in Edge Computing [J]. Computer Science, 2021, 48(1): 11-15.
[15] LIANG Jun-bin, TIAN Feng-sen, JIANG Chan, WANG Tian-shu. Survey on Task Offloading Techniques for Mobile Edge Computing with Multi-devices and Multi-servers in Internet of Things [J]. Computer Science, 2021, 48(1): 16-25.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!