Improving IoRT Networks: Cross-Tier Resource Allocation for Multi-Antenna UAV Relays in SAGIN
Bibliographic record
Abstract
In response to reducing power consumption while reducing equipment and maintaining high data rate requirements in Space-Air-Ground Integrated Networks (SAGIN) as part of the Internet of Remote Things (IoRT) networks, this paper proposes a system model and optimization scheme. Our modified architecture features multi-antenna Unmanned Aerial Vehicle (UAV) relays, gateway selection among existing UAVs for communication with satellite, and integration of Free Space Optic (FSO) space links to enhance air-space link data rates. The optimization problem aims to minimize the weighted sum of the number of gateways and the total UAV power consumption by jointly optimizing UAV deployment, UAV power allocation, gateway selection, and channel allocation. By jointly adopting proposed size-constrained PSO-K-means clustering, Simulated Annealing (SA) method, and Successive Convex Approximation (SCA) method, a two-stage scheme is devised to solve the proposed NP-hard and non-convex problem. Our simulation results indicate that an average performance improvement of 44% is achieved by our proposed scheme compared to the case where only clustering is performed. Furthermore, our proposed solution finds the number of gateways for different scenarios. For instance, almost all UAVs are selected as gateways to minimize UAV power consumption without considering any constraint on the number of gateways. Moreover, the performance difference between considering total UAV power consumption and only transmission power in the system optimization is shown. The results indicate that taking circuit power into account influences the selection of the number of gateways, as circuit power consumption is substantially higher than transmission power.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".