Self-Contained Dual-Input Interferometric Receiver for Paralleled-Multichannel Wireless Systems
Bibliographic record
Abstract
In this paper, a self-contained dual-input receiver architecture based on the interferometric technique is proposed and demonstrated for the first time for paralleled-multichannel wireless systems. Different from conventional counterparts, the proposed receiver consists of dual-input RF channel paths, and only one sole hardware is used to realize frequency translation, i.e., the conversion to intermediate frequency (IF) band. Demodulated IF signals can be extracted from two output ports instead of four ports in legacy multiport systems, thereby further reducing circuit complexity, size, cost, and power consumption. A mathematical model of the receiver architecture is formulated and applied to examine its modes of operation. For the proof of concept, a dual-band and dual-polarized prototype RX frontend is designed and fabricated to validate the proposed architecture. The transmission and demodulation of multiple digital modulation signals including QPSK, 16-QAM, 32-QAM, and 64-QAM are successfully demonstrated experimentally. Those measured results confirm that the proposed receiver architecture achieves good and desired performances. Based on the proposed self-contained dual-input receiver architecture, a multiband and multifunction polarization-diversified wireless system featuring compact size, low-cost, and low-power consumption suitable for 5G, 6G, and beyond can be realized.
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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) | 0.001 | 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".