Temat: beamforming - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Deep Learning-Powered Beamforming for 5G Massive MIMO Systems
Autorzy:: Bendjillali, Ridha Ilyas
Bendelhoum, Mohammed Sofiane
Tadjeddine, Ali Abderrazak
Kamline, Miloud
Powiązania:: https://bibliotekanauki.pl/articles/27312956.pdf
Data publikacji:: 2023
Wydawca:: Instytut Łączności - Państwowy Instytut Badawczy
Tematy:: 5G
digital beamforming
hybrid beamforming
massive MIMO
ResNeSt
Opis:: In this study, a ResNeSt-based deep learning approach to beamforming for 5G massive multiple-input multipleoutput (MIMO) systems is presented. The ResNeSt-based deep learning method is harnessed to simplify and optimize the beamforming process, consequently improving performance and efficiency of 5G and beyond communication networks. A study of beamforming capabilities has revealed potential to maximize channel capacity while minimizing interference, thus eliminating inherent limitations of the traditional methods. The proposed model shows superior adaptability to dynamic channel conditions and outperforms traditional techniques across various interference scenarios.
Źródło:: Journal of Telecommunications and Information Technology; 2023, 4; 38--45
1509-4553
1899-8852
Pojawia się w:: Journal of Telecommunications and Information Technology
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Power Scaling and Antenna Selection Techniques for Hybrid Beamforming in mmWave Massive MIMO Systems
Autorzy:: Nalband, Abdul Haq
Sarvagya, Mrinal
Ahmed, Mohammed Riyaz
Powiązania:: https://bibliotekanauki.pl/articles/226190.pdf
Data publikacji:: 2020
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: antenna selection
massive MIMO
millimeter Wave
5G
hybrid beamforming
power scaling
Opis:: With the advent of massive MIMO and mmWave, Antenna selection is the new frontier in hybrid beamforming employed in 5G base stations. Tele-operators are reworking on the components while upgrading to 5G where the antenna is a last-mile device. The burden on the physical layer not only demands smart and adaptive antennas but also an intelligent antenna selection mechanism to reduce power consumption and improve system capacity while degrading the hardware cost and complexity. This work focuses on reducing the power consumption and finding the optimal number of RF chains for a given millimeter wave massive MIMO system. At first, we investigate the power scaling method for both perfect Channel State Information (CSI) and imperfect CSI where the power is reduced by1/Nᵣ and 1/√Nᵣ respectively. We further propose to reduce the power consumption by emphasizing on the subdued resolution of Analog-to-Digital Converters (ADCs) with quantization awareness. The proposed algorithm selects the optimal number of antenna elements based on the resolution of ADCs without compromising on the quality of reception. The performance of the proposed algorithm shows significant improvement when compared with conventional and random antenna selection methods.
Źródło:: International Journal of Electronics and Telecommunications; 2020, 66, 3; 529-535
2300-1933
Pojawia się w:: International Journal of Electronics and Telecommunications
Dostawca treści:: Biblioteka Nauki

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