Multi-Frequency Radio Map Assisted Unmanned Aerial Relay for Bridging Ground D2D Networks
Bibliographic record
Abstract
In the rapidly advancing realm of wireless communication, device-to-device (D2D) technology, an emerging approach for data exchange and connectivity, has been attracting increasing attention. Unmanned Aerial Vehicles (UAVs) can act as air relays or base stations, and integrate isolated D2D clusters into a cohesive network fabric in outdoor environments. However, in complex terrain, the communication signals are subject to irregular attenuation, and the signal propagation attenuation of different frequency bands in the same terrain is inconsistent. It is challenging to utilize UAVs to coverage D2D terrestrial users in complex terrain. In this paper, we propose the UAVs relaying for bridging the terrestrial D2D networks assisted by multi-frequency radio maps. From the real-world topographical data, we generate multi-frequency radio maps, which represent the distortion of different frequency band signals by rich information about land layouts. Next, we focus on the air-to-ground D2D network topology and formulate it into an optimization problem. Then, we decompose it into two subproblems. The first subproblem pertains to the design of the ground network structure. We employ the D2D frequency band radio map to assess the communication quality between user pairs, and propose a measure of D2D closeness centrality to select ‘cellular users’ that can communicate directly to a UAV. The second subproblem involves the UAVs’ deployment and the frequency selection. We present a multi-frequency radio map improved k-means method, which has lower algorithm complexity than the traversal method by reducing the utilization of the radio maps. Simulations validate the proposed scheme, demonstrating that: 1. Multi-frequency radio maps can provide efficient gains with real-world complex topography; 2. The proposed network structure and algorithm outperform other existing approaches.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".