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Record W3173404813 · doi:10.35940/ijeat.e2832.0610521

Modeling and Designed of a Monopole Antenna that Operate at 3.3 [GHz] for Future 5G sub 6 [GHz]

2021· article· en· W3173404813 on OpenAlexaffabout
Gholam Aghashirin, Maged Kafafy, Hoda S. Abdel-Aty Zohdy, Mohamed Zohdy, Adam Timmons

Bibliographic record

VenueInternational Journal of Engineering and Advanced Technology · 2021
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAntenna (radio)MIMOWirelessMonopole antennaComputer scienceOmnidirectional antennaTelecommunicationsBeamforming

Abstract

fetched live from OpenAlex

Antenna unit is an important part of ADAS L2, L2+ and Automated Driving L3 systems. It needs to function as needed in dGPS, HD Map Correction Services, OEM Radios and Navigation Systems. The presented monopole antenna model for 5G below 6 [GHz] operating at 3.3 [GHz] is developed. This work demonstrates the modeling, design, and determining of monopole antenna with intended targeted applications within the automotive system emerging autonomous vehicles space and as well as 5G Wireless Cellular Technology domain. FEKO simulation is undertaken rather than mathematical modeling to create the structure and conduct the analysis of the proposed monopole antenna. In order to support the fifth generation (5G) of wireless communication networks, SOS messages, vehicle tracking, remote vehicle start, Advanced Driver Assistance Systems (ADAS) L2, L2+/ Autonomous Driving (AD) L3 systems self-driving vehicles powered by 5G with rapidly growing sets of ADAS and AD features and functions within the autonomous space, USA cellular carriers mobile phone communication standard 4G MISO and 5G MIMO, LTE1, LTE2, connected functions, features/services, IoT, DSRC, V2X, and C-V2X applications and 5G enable vehicles destined for the NAFTA (USA, Canada and Mexico) market, a new single monopole antenna that operate at 3.3 [GHz] for future 5G (MIMO) below 6 [GHz] modeling, design and simulation with intended automotive applicability and applications is proposed. The presented novel new 5G below 6 [GHz] monopole antenna: 1. Is not being investigated on the literatures review and published papers studied. 2. No paper exists on these frequency bands. 3. The desired monopole antenna is a new antenna with fewer components, reduction in size, low profile, competitive cost, better response to received RF signals for frequencies for future 5G below 6 [GHz] with each of the following: a. Range of operating frequencies, 0.6 [GHz] to 5.9256 [GHz]. b. Center frequency = 3.2628 [GHz] ~ 3.3 [GHz] for the above band. c. Lambda (λ) = (3.0 x10^8 [m/sec^2]) / (3.3x10^9 [Hz]) = 0.090 [m] = 90 [mm], lambda (λ) /4 = (0.090 [m])/4 = 0.0225 [m] = 22.5 mm ~ 23 [mm], the overall monopole antenna height. To be more direct, simulation studies are carried out and are done utilizing FEKO software package from Altair to model the proposed monopole antenna for 5G below 6 [GHz] frequency band. The focus is on the frequency band for 5G sub 6 [GHz] cellular system. The paper will introduce the following key points: 1. Modelled and anayzed single element 5G sub 6 [GHz] monopole antenna. 2. Student version of CAD FEKO program was used to design our desired monopole antenna with a wire feed excitation coupled with step-by-step instructions is undertaken to highlight the model geometry creation of our monopole antenna. POST FEKO program is used to plot and view our simulation results. 3. We report the development of 5G below 6 [GHz] for fifth generation (5G) system that meets automotive and vehicle homologation specification requirement of antenna height < 70 [mm]. So that the proposed monopole antenna can easly be integrated into multi tuned cellular antenna system. 4. The FEKO simulation is conducted in 2D and 3D element model, in terms of Far-Field Vertical Gain as a function of an Elevation Angle plots. 5. Future research work and study for the next steps will be recommended.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.745
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.215
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2021
Admission routes2
Has abstractyes

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