Advancing 5G Coverage: Novel Methods for Metasurface Characterization and Leaky-Wave Antenna Reconfigurability
Bibliographic record
Abstract
This thesis introduces novel approaches to enhance 5G coverage through accurate metasurface (MS) characterization using incident field reconstruction and pattern reconfigurability in leaky wave antennas (LWAs). Metasurfaces offer potential coverage enhancement by signal manipulation. The incident field reconstruction method efficiently analyzes and optimizes MSs, addressing traditional limitations. Using numerical superposition and mechanical displacement, it creates user-defined incident fields, reducing design-measurement discrepancies. This facilitates improved MS deployment for 5G coverage. Inspired by source displacement, a pattern reconfigurable LWA design with mechanical switching is proposed. In Ka-band, a contactless excitation method enables beam-steering, benefiting communication systems with cost-effective flexibility. This approach also facilitates beam scanning, bandwidth, and polarization reconfiguration. This thesis provides innovative solutions to wireless communication challenges, demonstrated through MS incident field reconstruction and LWA contactless excitation methods, promising more reliable and efficient 5G networks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".