Resource Allocation in Cognitive Radio-Enabled UAV Communication
Bibliographic record
Abstract
The deployment of unmanned aerial vehicles (UAVs) in wireless communications will be constrained in practice by the unavailability of frequency spectrum. Cognitive radio techniques are viewed to offer promising solutions in which a secondary UAV-based network can operate in a frequency band licensed to an existing terrestrial wireless network with minimal interference. We investigate the performance of a cognitive radio-enabled UAV network configuration in which the UAV is allowed to communicate with secondary ground terminals (SGTs) in the underlay mode in the licensed spectrum band. Optimization of the performance of the secondary network is considered in terms of maximizing the total throughput of the network subject to satisfying two constraints. The first constraint is imposed to prevent interference with the primary network while the second constraint ensures that the throughput requirement of each SGT is met. A probabilistic channel model is assumed. The Variables to be determined include the transmission power, the channel time allocations to the SGTs and the route and coordinates of the stationary locations in space in which the UAV will hover and transmit. A heuristic approach is developed in order to arrive at solutions to this extremely complex optimization problem and results of numerical simulations are presented.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".