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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".