BEAMFORMING IN INTELLIGENT ENVIRONMENTS BASED ON ULTRA-MASSIVE MIMO PLATFORMS IN MILLIMETERWAVE AND TERAHERTZ BANDS

被引:0
|
作者
Nie, Shuai [1 ]
Akyildiz, Ian F. [1 ]
机构
[1] Georgia Inst Technol, Broadband Wireless Networking Lab, Sch Elect & Comp Engn, Atlanta, GA 30332 USA
来源
2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING | 2020年
基金
美国国家科学基金会;
关键词
Ultra-massive MIMO; Millimeter wave; Terahertz-band communications; Beamforming; COMMUNICATION;
D O I
暂无
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
Recent techniques utilizing reflectarrays or novel metamaterial-based surfaces to control wireless propagation environments have attracted great attention. While most solutions focus on sub-6 GHz wireless channel to assist in throughput enhancement, these intelligent surfaces have significant potentials in combating the transmission distance limitation and solving the non-line-of-sight transmission problems for the millimeter wave (30-300 GHz) and Terahertz-band (0.3-10 THz) where signal propagation is significantly attenuated in the atmosphere. The Ultra-Massive MIMO (UM MIMO) communication framework has been proposed in such frequency bands, which relies on the plasmonic antenna arrays to realize different operation modes, including anomalous reflection, transmission and reception, among others. In this paper, a joint beamforming scheme is developed based on fractional programming optimization to maximize the spectral efficiency under practical consideration of energy constraints of the UM MIMO communication platform. Numerical results are presented to show the performance comparison with existing solutions.
引用
收藏
页码:8683 / 8687
页数:5
相关论文
共 50 条
  • [21] Massive MIMO Performance Comparison of Beamforming and Multiplexing in the Terahertz Band
    Hoseini, Sayed Amir
    Ding, Ming
    Hassan, Mahbub
    2017 IEEE GLOBECOM WORKSHOPS (GC WKSHPS), 2017,
  • [22] Reflecting Intelligent Surfaces Assisted High-Rank Ultra Massive MIMO Terahertz Channels
    Sheemar, Chandan Kumar
    Solanki, Sourabh
    Khan, Wali Ullah
    Abdullah, Zaid
    Lagunas, Eva
    Chatzinotas, Symeon
    2024 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC 2024, 2024,
  • [23] An Adaptive and Robust Deep Learning Framework for THz Ultra-Massive MIMO Channel Estimation
    Yu, Wentao
    Shen, Yifei
    He, Hengtao
    Yu, Xianghao
    Song, Shenghui
    Zhang, Jun
    Letaief, Khaled B.
    IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, 2023, 17 (04) : 761 - 776
  • [24] A Novel Ultra-Massive MIMO BDCM for 6G Wireless Communication Systems
    Zheng, Yi
    Wang, Cheng-Xiang
    Huang, Jie
    Feng, Rui
    Thompson, John S.
    IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2024, 23 (04) : 3221 - 3237
  • [25] Particle swarm optimization based beamforming in massive MIMO systems
    Kareem T.A.
    Hussain M.A.
    Jabbar M.K.
    International Journal of Interactive Mobile Technologies, 2020, 14 (05) : 176 - 192
  • [26] PRINCE: A Pruned AMP Integrated Deep CNN Method for Efficient Channel Estimation of Millimeter-Wave and Terahertz Ultra-Massive MIMO Systems
    Hu, Zhengdong
    Chen, Yuhang
    Han, Chong
    IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2023, 22 (11) : 8066 - 8079
  • [27] Autoencoder Neural Network Based Intelligent Hybrid Beamforming Design for mmWave Massive MIMO Systems
    Tao, Jiyun
    Chen, Jienan
    Xing, Jing
    Fu, Shengli
    Xie, Junfei
    IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING, 2020, 6 (03) : 1019 - 1030
  • [28] Mutual Coupling Analysis of 6G Ultra-Massive MIMO Channel Measurements and Models
    Feng, Rui
    Wang, Cheng-Xiang
    Huang, Jie
    Zheng, Yi
    Lai, Fan
    Zhou, Wenqi
    IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2022), 2022, : 956 - 961
  • [29] Measurements and Characteristics Analysis of 6G Ultra-Massive MIMO Wireless Channels With Different Antenna Configurations and Scenarios
    Zheng, Yi
    Wang, Cheng-Xiang
    Huang, Jie
    Feng, Rui
    Thompson, John
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2023, 72 (08) : 9720 - 9732
  • [30] Experience-driven learning-based intelligent hybrid beamforming for massive MIMO mmWave communications
    Arjoune, Youness
    Faruque, Saleh
    PHYSICAL COMMUNICATION, 2022, 51