High Data Rate Ka-band Beam Switching Antenna Network for Intelligent Satellite Communications
Bibliographic record
Abstract
This research paper presents an innovative and compact multi-port transceiver designed specifically for time division duplex (TDD) beam switching antenna networks, with a primary focus on high bandwidth and high data rate applications within the Ka-band satellite communication spectrum. The core innovation of this transceiver lies in its seamless integration of both uplink and downlink transmissions within a single six-port junction. This integration not only simplifies the overall system architecture but also enhances the efficiency of bidirectional communication. To validate the transceiver’s capabilities, extensive simulations were conducted using various PSK/QAM signal modulations, all operating within the challenging 28 GHz frequency band. These simulations aimed to assess the performance of both the multiport modulator and demodulator. The outcomes of these simulations unequivocally demonstrate the outstanding performance of the proposed multi-port transceiver. They further underscore the successful implementation of TDD beam switching antenna networks tailored for advanced satellite communication systems operating in the Ka-band. This breakthrough offers promising prospects for high-speed, high-capacity satellite data transmission in the near future.
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 | 0.000 |
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".