Analysis and Design of Low-Profile Reconfigurable Leaky-Wave Antennas Based on Substrate Integrated Waveguide for 5G Millimeter-Wave Applications
Bibliographic record
Abstract
5G millimeter-wave (mm-wave) applications have several challenges, such as detection accuracy, interference from other channels, high path loss, covering several users in a dense area, and vulnerability to the environmental conditions.Several antenna technologies were proposed in this dissertation to address some of the above-mentioned challenges in 5G applications with a focus on wireless networks and vehicle to everything (V2X) communications.Leaky-wave antennas (LWAs) are suitable candidates for 5G mm-wave applications due to their beam-scanning capability, compactness, low cost, and ease of fabrication.Substrate integrated waveguide (SIW) and half-mode substrate integrated waveguide (HMSIW) are suitable candidates for realizing LWAs because of their low-profile and integration capability.Several design approaches were introduced in this dissertation to improve the performance of SIW/HMSIW LWAs in 5G mm-wave applications.Some of the proposed antennas have a relatively wide beam-scanning range, suitable for radar systems and V2X communications, while others have small beam-squint, suitable for point-to-point communications and seeker antennas.Tapering the side aperture of an HMSIW and the feed transition of a SIW resulted in side-lobe level (SLL) reduction to enhance the detection accuracy and reduce sensitivity to interference.Applying the proposed methods reduced the SLL of an HMSIW LWA and a SIW LWA to -11.2 dB and -11.4 dB, respectively, in the mm-wave frequency band.iii Furthermore, embedding cavities into a compact low temperature co-fired ceramics (LTCC) antenna enhanced the gain to 7.6 dBi at 28.5 GHz.Implementing different types of reconfigurable structures resulted in electronic beamscanning, which is the most suitable approach to provide coverage for several users in dense areas due to its ease of implementation.Each of the proposed reconfigurable antennas posed different scanning ranges.One example used varactor diodes for tuning the antenna and achieved 30⁰ of beam-scanning range by varying the varactor diode's capacitance in the range of 200-500fF.Moreover, the bias circuitry was integrated into the RF ground in a few designs to miniaturize the reconfigurable antenna.The compactness, beam-scanning capability, low SLL, medium to high gain, and low fabrication cost are among the features that make the proposed antennas suitable candidates for 5G mm-wave applications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".