Nothing Special   »   [go: up one dir, main page]

Medra et al., 2023 - Google Patents

New Machine Learning Approach for Low Overhead Multi-Beam Prediction

Medra et al., 2023

Document ID
6917088661171683177
Author
Medra M
Wei H
Luong P
Mohammadhadi H
Publication year
Publication venue
2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

External Links

Snippet

This paper investigates the problem of initial beam alignment that is crucial to beam-based communications used in 5G and envisioned for 6G. Our proposed method includes a beam sweeping using a learned codebook, and a beam prediction by a categorization neural …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04BTRANSMISSION
    • H04B7/00Radio transmission systems, i.e. using radiation field
    • H04B7/02Diversity systems; Multi-antenna systems, i.e. transmission or reception using multiple antennas
    • H04B7/04Diversity systems; Multi-antenna systems, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06Diversity systems; Multi-antenna systems, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613Diversity systems; Multi-antenna systems, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615Diversity systems; Multi-antenna systems, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0619Diversity systems; Multi-antenna systems, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
    • H04B7/0621Feedback content
    • H04B7/0634Antenna weights or vector/matrix coefficients
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04BTRANSMISSION
    • H04B7/00Radio transmission systems, i.e. using radiation field
    • H04B7/02Diversity systems; Multi-antenna systems, i.e. transmission or reception using multiple antennas
    • H04B7/04Diversity systems; Multi-antenna systems, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413MIMO systems
    • H04B7/0426Power distribution
    • HELECTRICITY
    • H01BASIC ELECTRIC ELEMENTS
    • H01QAERIALS
    • H01Q3/00Arrangements for changing or varying the orientation or the shape of the directional pattern of the waves radiated from an aerial or aerial system
    • H01Q3/26Arrangements for changing or varying the orientation or the shape of the directional pattern of the waves radiated from an aerial or aerial system varying the relative phase or relative amplitude of energisation between two or more active radiating elements; varying the distribution of energy across a radiating aperture
    • H01Q3/2605Array of radiating elements provided with a feedback control over the element weights, e.g. adaptive arrays

Similar Documents

Publication Publication Date Title
US11304063B2 (en) Deep learning-based beamforming communication system and method
Echigo et al. A deep learning-based low overhead beam selection in mmWave communications
Chen et al. Hybrid spherical-and planar-wave modeling and DCNN-powered estimation of terahertz ultra-massive MIMO channels
Rezaie et al. Location-and orientation-aided millimeter wave beam selection using deep learning
Heng et al. Learning site-specific probing beams for fast mmWave beam alignment
WO2020253156A1 (en) Data-driven beam tracking method and apparatus for mobile millimeter wave communication system
Jiang et al. Digital twin based beam prediction: Can we train in the digital world and deploy in reality?
Wang et al. Site-specific online compressive beam codebook learning in mmWave vehicular communication
Chou et al. Fast position-aided MIMO beam training via noisy tensor completion
Chen et al. Computer vision aided codebook design for MIMO communications systems
Shtaiwi et al. RIS-assisted mmWave channel estimation using convolutional neural networks
Yajnanarayana et al. Multistatic sensing of passive targets using 6G cellular infrastructure
Rezaie et al. Deep transfer learning for location-aware millimeter wave beam selection
Heng et al. Learning probing beams for fast mmWave beam alignment
Garkisch et al. Codebook-based user tracking in irs-assisted mmwave communication networks
Ruah et al. Calibrating wireless ray tracing for digital twinning using local phase error estimates
Huang et al. A Frequency Domain Predictive Channel Model for 6G Wireless MIMO Communications Based on Deep Learning
CN115622596B (en) Rapid beam alignment method based on multi-task learning
Medra et al. New Machine Learning Approach for Low Overhead Multi-Beam Prediction
Aquino et al. A Review of Direction of Arrival Estimation Techniques in Massive MIMO 5G Wireless Communication Systems
Wang et al. Deep learning-based compressive beam alignment in mmWave vehicular systems
Nguyen et al. Millimeter-wave received power prediction from time-series images using deep learning
Li et al. A GAN-GRU Based Space-Time Predictive Channel Model for 6G Wireless Communications
Becker Dynamic beamforming optimization for anti-jamming and hardware fault recovery
Liu et al. DNN-based beam and blockage prediction in 3GPP InH scenario