Low-Cost Multiband Four-Port Phased Array Antenna for Sub-6 GHz 5G Applications With Enhanced Gain Methodology in Radio-Over-Fiber Systems Using Modulation Instability
Bibliographic record
Abstract
Phased array antenna (PAA) technology is essential for applications requiring high gain and wide bandwidth, such as sensors, medical, and 5G. Achieving such a design, however, is a challenging and intricate process that calls for precise calculations and a combination of findings to alter the phase and amplitude of each unit. Furthermore, coupling effects between these PAA structure elements can only be completed with the use of full-wave electromagnetic simulation tools. Due to recent advances, radio-over-fiber (RoF) technology has been positioned as a possible alternative for high-capacity wireless communications. This paper presents a low-cost, multiband Sub-6 GHz 5G PAA with enhanced gain achieved through integration with a new specialized RoF system design to improve PAA performance by using the phenomenon of modulation instability (MI). Optimizing the antenna’s Defected Ground Structure (DGS) leads to even more improvement. To enable operation across three distinct frequency bands (Sub-6 GHz n78 band (3-3.8 GHz), n79 band (3.8-5 GHz), and n46 band (5-5.5 GHz)), the proposed antenna design features four elliptical patches strategically positioned at the four sides of the ground plane, providing comprehensive 360° coverage in the azimuth plane. Additionally, integrating elliptical slots and upper gaps contributes to improvement. The proposed PAA’s experimentally validated gain values are 5.2 dB, 7.4 dB, and 7.8 dB in the n78, n79, and n46 bands, respectively. For improving the performance of the proposed PAA in RoF systems anomalous fibers ($n_{2} \neq 0$and$\beta _{2} \lt 0$) are employed to consider the modulation instability (MI) phenomenon, which can lead to the generation of the MI gain on the carrier sideband. The true time delay (TTD) technique controls the beam pattern by adjusting the time delay between adjacent radiation elements. Furthermore, the TTD technique utilizes frequency combs for the proposed 4-element array antenna to apply MI gain to all antenna elements.
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 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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".