All-in-One Dual-Polarization Waveguide Receiver for Multichannel Wireless Systems
Bibliographic record
Abstract
In this work, a monoblock hardware receiver solution for dual-polarization multichannel wireless systems is proposed and demonstrated for the first time. The receiver benefits from the use of a square waveguide scheme enabling dual-polarization features. Two orthogonalized polarizations, namely, horizontally and vertically polarized waves, can simultaneously be excited in a single-modular hardware receiver while maintaining reliable performances. Compared to conventional dual polarization receiver architectures with two separate hardware platforms, the proposed receiver is set to greatly reduce the circuit complexity, size, cost, and power consumption. Besides, due to the self-contained dual-input radio frequency (RF) channels, paralleled-multiband operation can also be realized. The core multiport circuit consists of integrated dual polarization components, namely, cruciform couplers, orthomode transducers (OMTs), and phase shifters. The operating mechanism of the receiver is studied and explained with a mathematical model. To validate the concept, an experimental prototype is developed, fabricated, and measured. Paralleled-multichannel transmission of variousM-quadratic-amplitude modulation (QAM) signals is successfully demodulated experimentally. It is found that the proposed waveguide receiver presents a viable candidate for future multistandard wireless systems.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".