Deep Q-learning-enabled Deployment of Aerial Base Stations in the Presence of Mobile Users
Bibliographic record
Abstract
Uncrewed aerial vehicle-mounted base stations (UAV-BS) have recently attracted significant attention in order to assist ground base stations (BSs) and provide Internet access to users. UAV-BSs benefit from their mobility nature in the air and are able to constantly move towards the locations where the demand is higher. However, finding the optimal location of UAV-BSs and maintaining it is an NP-hard problem that has no deterministic solution in polynomial time. In this paper, we exploit reinforcement learning (RL) in order to solve the optimization problem of UAV-BSs and find their optimal location in the presence of mobile User Equipment (UEs). We consider UAV-BS as the agent of RL and deploy two algorithms, i.e. Q-learning and deep Q-learning in order to solve the location optimization problem of UAV-BSs. Through simulations, we show that the proposed DQL model with a continuous state space including the mobility information of users can effectively adapt to the environmental changes and improve the user data rate by 46%, packet loss ratio by 70%, and transmission delay by 60%.
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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".