Resource Allocation Approaches for Two-Tiers Machine-to-Machine Communications in an Interference Limited Environment

被引:7
|
作者
Bartoli, Giulio [1 ]
Fantacci, Romano [1 ]
Marabissi, Dania [1 ]
机构
[1] Univ Florence, Dept Informat Engn, I-50139 Florence, Italy
来源
IEEE INTERNET OF THINGS JOURNAL | 2019年 / 6卷 / 05期
关键词
Machine-type-communications (MTCs); random access (RA); resource allocation; RANDOM-ACCESS; PERFORMANCE ANALYSIS; DATA AGGREGATION; NETWORKS; LTE; PROTOCOL; DESIGN;
D O I
10.1109/JIOT.2019.2927812
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
One of the main challenges of supporting massive machine-type-communications (MTCs) by future mobile networks, is the radio access network (RAN) congestion. A viable solution is a hierarchical architecture where cluster-grouped machines are indirectly connected to the mobile network through a cluster gateway. This paper considers such an architecture and focuses on radio resource allocation to machine-clusters where resources can be spatially reused for concurrent transmissions. In particular, resources can be accessed simultaneously by machines of different clusters and by different communication phases, thus resulting in an interference-limited system. Three different resource partitioning schemes are proposed and evaluated, mainly differing on how interference is taken into account. The aim is to minimize the packet loss probability, and thus maximizing the spectral efficiency. Numerical results show the effectiveness of the proposed schemes in comparison to benchmark methods. In particular, by joining resource reuse and interference-mitigation, the efficiency improves significantly.
引用
收藏
页码:9112 / 9122
页数:11
相关论文
共 50 条
  • [41] A Survey on LTE/LTE-A Radio Resource Allocation Techniques for Machine-to-Machine Communication for B5G Networks
    Singh, Upendra
    Dua, Amit
    Tanwar, Sudeep
    Kumar, Neeraj
    Alazab, Mamoun
    IEEE ACCESS, 2021, 9 : 107976 - 107997
  • [42] Deep Reinforcement Learning-based resource allocation strategy for Energy Harvesting-Powered Cognitive Machine-to-Machine Networks
    Xu, Yi-Han
    Tian, Yong-Bo
    Searyoh, Prosper Komla
    Yu, Gang
    Yong, Yueh-Tiam
    COMPUTER COMMUNICATIONS, 2020, 160 : 706 - 717
  • [43] QoS-Aware Splitting and Radio Resource Allocation for Machine Type Communications
    Amitu, David Martin
    Akol, Roseline Nyongarwizi
    Nakeba, Peter
    2018 IEEE 8TH ANNUAL COMPUTING AND COMMUNICATION WORKSHOP AND CONFERENCE (CCWC), 2018, : 941 - 947
  • [44] Dynamic resource allocation and interference coordination for millimeter wave communications in dense urban environment
    Yagcioglu, Mert
    TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES, 2022, 33 (05):
  • [46] Machine Learning-based Resource Allocation for Multi-UAV Communications System
    Chang, Zheng
    Guo, Wenlong
    Guo, Xijuan
    Ristaniemi, Tapani
    2020 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS WORKSHOPS (ICC WORKSHOPS), 2020,
  • [47] Universal Anesthesia Machine: Clinical Application in an Austere, Resource-Limited Environment
    Vande Lune, Stefani A.
    Lantry, James H.
    Mason, Phillip E.
    Skupski, Richard
    Toth, Arthur
    Zimmer, Donald
    Mulligan, John
    McCurdy, Michael T.
    Larson, Emilee E.
    Preuss, Fletcher
    Tran, Quincy K.
    MILITARY MEDICINE, 2020, 185 (5-6) : E550 - E556
  • [48] The virtual machine resource allocation based on service features in cloud computing environment
    College of Information Engineering, Zhongzhou University, Zhengzhou
    450044, China
    Open. Cybern. Syst. J., (639-647):
  • [49] Resource Allocation for Ultradense Networks With Machine-Learning-Based Interference Graph Construction
    Cao, Jiaqi
    Peng, Tao
    Liu, Xin
    Dong, Weiguo
    Duan, Ran
    Yuan, Yannan
    Wang, Wenbo
    Cui, Shuguang
    IEEE INTERNET OF THINGS JOURNAL, 2020, 7 (03): : 2137 - 2151
  • [50] Flexible Radio Resource Allocation for Machine Type Communications in 5G Cellular Networks
    Hussien, Zaid Haj
    Sadi, Yalcin
    2018 26TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2018,