A 5.8 GHz Array Antenna Based on 4x4 Butler Matrix for Beamforming in 5G Network
Bibliographic record
Abstract
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 %.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".