Delay root cause analysis and 3D modeling of LTE control communication using machine learning
| dc.contributor.advisor | Ilyas, Muhammad | |
| dc.contributor.author | Mohammed, Sameer Qutaiba | |
| dc.date.accessioned | 2023-09-06T08:57:18Z | |
| dc.date.available | 2023-09-06T08:57:18Z | |
| dc.date.issued | 2022 | en_US |
| dc.date.submitted | 2022 | |
| dc.department | Enstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı | en_US |
| dc.description.abstract | This thesis examines 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 this work has an independent technique that does not employ LTE standards. Input-output MIMO with 512 elements for 100-GHz and 128 elements for mid-band sub-6-GHz has been used. 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). Rx and Tx activities at 0.5 km and 15.25 m altitude. Distance, handover authentication, rain, atmosphere, and sub-6GHz vs 100GHz weather conditions effect pathloss. Enhancing transmission power and efficiency improved spatial variety. 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 questionable 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 dataset with an LTE communication level of exactness of downpour assimilation and abundance foliage mis fort. | en_US |
| dc.identifier.citation | Mohammed, Sameer Qutaiba. (2022). Delay root cause analysis and 3D modeling of LTE control communication using machine learning. (Yayınlanmamış yüksek lisans tezi). Altınbaş Üniversitesi, Lisansüstü Eğitim Enstitüsü, İstanbul. | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.12939/3819 | |
| dc.identifier.yoktezid | 798232 | |
| dc.institutionauthor | Mohammed, Sameer Qutaiba | |
| dc.language.iso | en | |
| dc.publisher | Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü | en_US |
| dc.relation.publicationcategory | Tez | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | LTE | en_US |
| dc.subject | Investigation | en_US |
| dc.subject | MIMO | en_US |
| dc.subject | Artificial Neural Network | en_US |
| dc.subject | Antennas | en_US |
| dc.title | Delay root cause analysis and 3D modeling of LTE control communication using machine learning | |
| dc.type | Master Thesis |
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