Delay root cause analysis and 3D modeling of LTE control communication using machine learning
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Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This study investigates and evaluates delay root cause analysis and 3D modeling of LTE control communication utilizing sophisticated machine learning for network testing. The research studied LTE protocols for 5th-generation mobile telephony and provided guidelines for controlling LTE frequency for background knowledge, although it used an independent technique that did not employ LTE standards. 512 elements of input-output MIMO were employed for 100-GHz and 128 elements for mid-band sub-6-GHz. LOS is always 0.5. This paper is about LTE, not 3D modeling of LTE control path loss type communication using machine learning. This work's route loss depends on cross-pol beam LTE polarization (±45o). The receiver (Rx) operations and transmitter (Tx) activities in the estimated distance of 0.5 km at an approximate altitude of 15.25 m. Distance, handover authentication, rain, atmosphere, and sub-6GHz vs 100GHz weather conditions affect path loss. The methodology has enhanced the spatial variety by boosting transmitting power and transmitting efficiency. Authorizing and sanctioning ANN-based LTE frequency for both mid-band sub-6-GHz and 100-GHz is possible due to its planning and development using open-source material and strategy with high transmission power and rate under doubtful handover confirmation using MIMO input/yield receiving wires. This theory examines LTE innovation dimensioning as unbiased for various handover verification and allows input boundary alterations for various organization arrangement setups for LTE recurrent data transmission from 6 GHz to 100 GHz for three climate sorts. This cycle should be seen as an undeniable level way to examine LTE networks under various air conditions. Using signal handling tool compartment and explicit AI-based ANN calculation from AI toolkit in MATLAB R2019a, it is possible to create a result answer for three climate types in a dataset with an LTE communication level of exactness of downpour assimilation and abundance foliage miss fort.
Açıklama
Anahtar Kelimeler
Antennas, Artificial neural network, Authentication, Investigation, MIMO
Kaynak
Proceedings - 2022 International Conference on Artificial Intelligence of Things, ICAIoT 2022
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
Sayı
Künye
Mohammed, S. Q., & Ilyas, M. (2022). Delay root cause analysis and 3D modeling of LTE control communication using machine learning. In 2022 International Conference on Artificial Intelligence of Things (ICAIoT). IEEE.
