Beam Training and Allocation for Multiuser Millimeter Wave Massive MIMO Systems

被引:61
|
作者
Sun, Xuyao [1 ]
Qi, Chenhao [1 ]
Li, Geoffrey Ye [2 ]
机构
[1] Southeast Univ, Sch Informat Sci & Engn, Nanjing 210096, Jiangsu, Peoples R China
[2] Georgia Inst Technol, Sch Elect & Comp Engn, Atlanta, GA 30332 USA
基金
中国国家自然科学基金;
关键词
Millimeter wave (mm-wave) communications; massive MIMO; beam training; beam allocation; CHANNEL ESTIMATION; 5G; COMPLEXITY;
D O I
10.1109/TWC.2018.2889071
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
We investigate beam training and allocation for multiuser millimeter wave massive MIMO systems. An orthogonal pilot-based beam training scheme is first developed to reduce the number of training times, where all users can simultaneously perform the beam training with the base station (BS). As the number of users increase, the same beam from the BS may point to different users, leading to beam conflict and multiuser interference. Therefore, a quality-of-service (QoS) constrained (QC) beam allocation scheme is proposed to maximize the equivalent channel gain of the QoS-satisfied users, under the premise that the number of the QoS-satisfied users without beam conflict is maximized. To reduce the overhead of beam training, two partial beam training schemes, an interlaced scanning (IS)-, and a selection probability (SP)-based schemes, are proposed. The overhead of beam training for the IS-based scheme can be reduced by nearly half, while the overhead for the SP-based scheme is flexible. The simulation results show that the QC-based beam allocation scheme can effectively mitigate the interference caused by the beam conflict and significantly improve the spectral efficiency, while the IS-based and SP-based schemes significantly reduce the overhead of beam training at the cost of sacrificing spectral efficiency, a little.
引用
收藏
页码:1041 / 1053
页数:13
相关论文
共 50 条
  • [41] COMPRESSIVE SENSING BASED INITIAL BEAMFORMING TRAINING FOR MASSIVE MIMO MILLIMETER-WAVE SYSTEMS
    Yan, Han
    Cabria, Danijela
    2016 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP), 2016, : 620 - 624
  • [42] Position-Aided Fast Beam Training in mm-Wave Multiuser MIMO Systems
    Xie, J.
    Jing, X.
    Huang, H.
    JOURNAL OF COMMUNICATIONS TECHNOLOGY AND ELECTRONICS, 2019, 64 (12) : 1391 - 1397
  • [43] Position-Aided Fast Beam Training in mm-Wave Multiuser MIMO Systems
    J. Xie
    X. Jing
    H. Huang
    Journal of Communications Technology and Electronics, 2019, 64 : 1391 - 1397
  • [44] Detection for Hybrid Beamforming Millimeter Wave Massive MIMO Systems
    Izadinasab, Kazem
    Shaban, Ahmed Wagdy
    Damen, Oussama
    IEEE COMMUNICATIONS LETTERS, 2021, 25 (04) : 1168 - 1172
  • [45] Angle Domain Channel Estimation in Hybrid Millimeter Wave Massive MIMO Systems
    Fan, Dian
    Gao, Feifei
    Liu, Yuanwei
    Deng, Yansha
    Wang, Gongpu
    Zhong, Zhangdui
    Nallanathan, Arumugam
    IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2018, 17 (12) : 8165 - 8179
  • [46] Relay Hybrid Precoding Design in Millimeter-Wave Massive MIMO Systems
    Xue, Xuan
    Wang, Yongchao
    Dai, Linglong
    Masouros, Christos
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2018, 66 (08) : 2011 - 2026
  • [47] Deep Learning for Direct Hybrid Precoding in Millimeter Wave Massive MIMO Systems
    Li, Xiaofeng
    Alkhateeb, Ahmed
    CONFERENCE RECORD OF THE 2019 FIFTY-THIRD ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS, 2019, : 800 - 805
  • [48] Millimeter-Wave Massive MIMO Communication for Future Wireless Systems: A Survey
    Busari, Sherif Adeshina
    Huq, Kazi Mohammed Saidul
    Mumtaz, Shahid
    Dai, Linglong
    Rodriguez, Jonathan
    IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2018, 20 (02): : 836 - 869
  • [49] Downlink Training Sequence Design for FDD Multiuser Massive MIMO Systems
    Bazzi, Samer
    Xu, Wen
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2017, 65 (18) : 4732 - 4744
  • [50] Decision Fusion in Centralized and Distributed Multiuser Millimeter-Wave Massive MIMO-OFDM Sensor Networks
    Kumar, Palla Siva
    Chawla, Apoorva
    Srivastava, Suraj
    Jagannatham, Aditya K.
    Hanzo, Lajos
    IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY, 2024, 5 : 185 - 201