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A 5.8 GHz Array Antenna Based on 4x4 Butler Matrix for Beamforming in 5G Network

2022· article· en· W4310880281 on OpenAlex
Maryam Eshaghi, Rashid Rashidzadeh

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2022 IEEE Sensors · 2022
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsBeamformingAntenna (radio)Antenna arrayOmnidirectional antennaElectrical engineeringComputer scienceEnergy harvestingMicrostrip antennaSmart antennaEffective radiated powerElectronic engineeringRadiation patternTelecommunicationsPower (physics)EngineeringPhysics

Abstract

fetched live from OpenAlex

Due to the fast development of the fifth-generation (5G) wireless network and high-speed data communications, a vast number of Internet of Things (IoT) sensors have been deployed most of them battery powered. Utilizing wireless energy harvesting techniques to power up IoT sensors eliminates the constraints of batteries and their limited lifetime as an energy source. A multi-beam array antenna has the potential to focus the radiation to the desired location and provide an adequate amount of energy to turn on a low-power IoT sensor. In this paper, a new 4 X 4 Butler matrix beamforming network is designed on FR-4 material which is connected to a 2x4 microstrip array patch antenna at a 5.8 GHz frequency. Each output of the Butler Matrix can provide power to two antenna elements simultaneously. The array antenna and Bulter Matrix network are designed and simulated in COMSOL Multiphysics tools. The simulation results indicate that the Butler matrix equally divides the voltage of -6 dB to all output ports when the input ports are excited. The array antenna represents the maximum effective radiated power of - 11dB at 5.8 GHz. The maximum antenna gain is 28 dBi with a radiation efficiency of 86 %.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.220
Teacher spread0.210 · 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