Reinforcement Learning-Based Trajectory Design for the Aerial Base\n Stations
Bibliographic record
Abstract
In this paper, the trajectory optimization problem for a multi-aerial base\nstation (ABS) communication network is investigated. The objective is to find\nthe trajectory of the ABSs so that the sum-rate of the users served by each ABS\nis maximized. To reach this goal, along with the optimal trajectory design,\noptimal power and sub-channel allocation is also of great importance to support\nthe users with the highest possible data rates. To solve this complicated\nproblem, we divide it into two sub-problems: ABS trajectory optimization\nsub-problem, and joint power and sub-channel assignment sub-problem. Then,\nbased on the Q-learning method, we develop a distributed algorithm which solves\nthese sub-problems efficiently, and does not need significant amount of\ninformation exchange between the ABSs and the core network. Simulation results\nshow that although Q-learning is a model-free reinforcement learning technique,\nit has a remarkable capability to train the ABSs to optimize their trajectories\nbased on the received reward signals, which carry decent information from the\ntopology of the network.\n
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".