On the Coordinated Multipoint Joint Transmission in Multi-UAV Sensor Networks
Bibliographic record
Abstract
The realization of the UAV-assisted sensing networks is subject to the establishment of seamless connectivity while attaining a decent power and spectral efficiency. In this paper, a network of sensing UAVs is considered where we propose techniques to efficiently recover data in an asynchronous coordinated multipoint (CoMP) scheme. Due to the fast-changing topology in UAV networks, the delays from UAVs to the access point, in general, may exceed cyclic prefix (CP) length, causing symbol-asynchronous reception at the receiver. We investigate this problem in an information theoretic approach. We derive the capacity region and show that by exploiting the asynchronous channels’ memory and correlation, the spectral efficiency can exceed that of non-cooperative reception, provided that the proper receiver is used. We characterize the mathematical model for the asynchronous fading channel in the context of a CoMP network. Moreover, we show that while successive interference cancelation (SIC) receiver is sensitive to the non-coherent reception, the performance elevates in the iterative joint detection and decoding (IJDD) receiver. We use a low-complexity detection scheme targeting minimizing the mean square error (MMSE) and adopt it in our asynchronous fading channel model. Our extensive simulations validate the proposed scheme providing a considerable boost in the channel reliability while increasing the spectral and power efficiency, even as the number of UAVs increases.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".