Reinforcement Learning Placement Algorithm for Optimization of UAV Network in Wireless Communication
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) or drones have attracted much attention in wireless communication networks because of their agility, unique flexibility, low cost of implementation, and the high strength of the line-of-sight (LoS) channel. They are widely used in different scenarios. In many environments with complex geographical conditions or in situations where areas are affected by natural disasters, UAVs can be used as base stations (BSs) for downlink ground users. The article proposes a communication system using multiple UAV-mounted BSs to improve coverage rate and minimize the number of required UAVs. The problem is formulated as a mixed-integer programming problem with constraints on the quality of service (QoS) and serviceability of each UAV. A three-step method is developed to solve the problem, which includes deriving the maximum service radius of UAVs using the Karush-Kuhn-Tucker (KKT) method, minimizing the number of required UAVs using reinforcement learning (RL) algorithm, and designing the three-dimensional (3D) position and frequency band of each UAV to increase signal power and reduce interference. The simulation results show that the RLP algorithm outperforms other algorithms in terms of coverage rate, user clustering, increased signal, reduced interference, and processing time required to find the optimal solution.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 0.001 |
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".