3D On and Off-Grid Dynamic Channel Tracking for Multiple UAVs and Satellite Communications
Why this work is in the frame
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Bibliographic record
Abstract
The space-air-ground integrated network (SAGIN) has drawn increasing attention for its benefits, such as wide coverage, high throughput for 5G and 6G communications. As one of the links, space-air communications between multiple unmanned aerial vehicles (UAVs) and Ka-band orbiting low earth orbit (LEO) satellites face a crucial challenge in tracking the 3D dynamic channel information. This paper exploits a statistical dynamic channel model called the multi-dimensional Markov model (MD-MM), which investigates the more realistic spatial and temporal correlation in the sparse UAVs-satellite channel. Specifically, the spatial and temporal probabilistic relationships of multi-user (MU) hidden support vector, single-user (SU) joint hidden support vector, and SU hidden value vector are investigated. The specific transition probabilities that connect the SU and MU hidden support vector for both azimuth and elevation directions are defined. Moreover, based on the proposed MD-MM, we derive a novel multi-dimensional dynamic turbo approximate message passing (MD-DTAMP) algorithm for tracking the 3D dynamic channel in multiple UAVs systems. Furthermore, we also develop a gradient update scheme to recursively find the azimuth and elevation offset for 3D off-grid estimation. Numerical results verify that the proposed algorithm shows superior 3D channel tracking performance with smaller pilot overhead and comparable complexity.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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) | 0.000 | 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 it