Multiconnectivity in Multicellular, Multiuser Systems: A Matching-Based Approach

被引:29
|
作者
Simsek, Meryem [1 ,2 ]
Hoessler, Tom [1 ]
Jorswieck, Eduard [1 ]
Klessig, Henrik [2 ]
Fettweis, Gerhard [1 ]
机构
[1] Tech Univ Dresden, Dept Elect Engn, D-01069 Dresden, Germany
[2] Int Comp Sci Inst, Berkeley, CA 94704 USA
基金
美国国家科学基金会;
关键词
Diversity; fifth generation (5G); many-to-many matching; matching theory; multiconnectivity; reliability; wireless systems; COORDINATED MULTIPOINT TRANSMISSION; DISTRIBUTED RESOURCE-ALLOCATION; LOW-LATENCY; DUAL CONNECTIVITY; AVAILABILITY ANALYSIS; COLLEGE ADMISSIONS; COMP; NETWORKS; COMMUNICATION; OPTIMIZATION;
D O I
10.1109/JPROC.2018.2887265
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Wireless communication systems have been evolving since the first generation. With the fifth generation of wireless systems, not only the evolutionary aspect of increased data rates is tackled but also the revolutionary aspect. Here, emerging use cases such as massive machine-type communication and ultrareliable low-latency communication will play a crucial role. Within this context, applications with stringent latency and reliability requirements are emerging. Wireless reliability is understood as successfully transmitting the desired amount of data within a given time. Diversity techniques, such as multiconnectivity, are potential solutions to achieve stringent reliability requirements. However, in a multi-user scenario, in which resources are shared, this might not always be possible. In this paper, we discuss the feasibility of various multiconnectivity approaches and propose a matching theory-based algorithm together with a novel scheduler aiming to guarantee the reliability requirements of as many users as possible in a multicellular, multiuser system. System-level simulations demonstrate that the proposed approach achieves 100% reliability for the fifth-percentile users in a highly loaded system. The maximum gain of fifth-percentile user throughput as compared to a static multiconnectivity approach is 150%.
引用
收藏
页码:394 / 413
页数:20
相关论文
共 50 条
  • [11] Matching-Based Capture-the-Flag Games for Multiagent Systems
    Wang, Jiali
    Zhou, Zhao
    Jin, Xin
    Mao, Shuai
    Tang, Yang
    [J]. IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, 2024, 16 (03) : 993 - 1005
  • [12] A Fuzzy Content Matching-based e-Commerce Recommendation Approach
    Mao, Mingsong
    Lu, Jie
    Zhang, Guangquan
    Zhang, Jinlong
    [J]. 2015 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE 2015), 2015,
  • [13] Licensed and Unlicensed Bands Allocation for Cellular Users: A Matching-Based Approach
    Gao, Yuan
    Wu, Yue
    Hu, Haonan
    Chu, Xiaoli
    Zhang, Jie
    [J]. IEEE WIRELESS COMMUNICATIONS LETTERS, 2019, 8 (03) : 969 - 972
  • [14] A Probabilistic Approach to Cross-Region Matching-Based Image Retrieval
    Gao, Zhimin
    Wang, Lei
    Zhou, Luping
    [J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2019, 28 (03) : 1191 - 1204
  • [15] WLCSSLearn: Learning Algorithm for Template Matching-based Gesture Recognition Systems
    Ciliberto, Mathias
    Cuspinera, Luis Ponce
    Roggen, Daniel
    [J]. 2019 JOINT 8TH INTERNATIONAL CONFERENCE ON INFORMATICS, ELECTRONICS & VISION (ICIEV) AND 2019 3RD INTERNATIONAL CONFERENCE ON IMAGING, VISION & PATTERN RECOGNITION (ICIVPR) WITH INTERNATIONAL CONFERENCE ON ACTIVITY AND BEHAVIOR COMPUTING (ABC), 2019, : 91 - 96
  • [16] A matching-based view interpolation scheme
    Sun, XY
    Dubois, E
    [J]. 2005 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1-5: SPEECH PROCESSING, 2005, : 877 - 880
  • [17] MATCHING-BASED HEDONIC SCALING IN PIGEON
    MILLER, HL
    [J]. JOURNAL OF THE EXPERIMENTAL ANALYSIS OF BEHAVIOR, 1976, 26 (03) : 335 - 347
  • [18] Characterization and Automation of Matching-Based Neighborhoods
    Benoist, Thierry
    [J]. INTEGRATION OF AI AND OR TECHNIQUES IN CONSTRAINT PROGRAMMING FOR COMBINATORIAL OPTIMIZATION PROBLEMS, 2010, 6140 : 45 - 50
  • [19] Learning-Based Ad Auction Design with Externalities: The Framework and A Matching-Based Approach
    Li, Ningyuan
    Ma, Yunxuan
    Zhao, Yang
    Duan, Zhijian
    Chen, Yurong
    Zhang, Zhilin
    Xu, Jian
    Zheng, Bo
    Deng, Xiaotie
    [J]. PROCEEDINGS OF THE 29TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2023, 2023, : 1291 - 1302
  • [20] Task Offloading in Wireless Powered Mobile Crowd Sensing: A Matching-Based Approach
    Yi, Difei
    Li, Jun
    Tang, Chengpei
    Lin, Ziqi
    Han, Yu
    Qiu, Rui
    [J]. ELECTRONICS, 2022, 11 (15)