A Continuous Actor–Critic Deep Q-Learning-Enabled Deployment of UAV Base Stations: Toward 6G Small Cells in the Skies of Smart Cities
Bibliographic record
Abstract
Uncrewed aerial vehicle-mounted base stations (UAV-BSs), also know as drone base stations, are considered to have promising potential to tackle the limitations of ground base stations. They can provide cost-effective Internet connection to es that are out of infrastructure. They can also take over quickly as service providers when ground base stations fail in an unanticipated manner. UAV-BSs benefit from their mobile nature that enables them to change their 3D locations if the demand profile changes rapidly. In order to effectively leverage the mobility of UAV-BSs so as to maximize the performance of the network, 3D location of UAV-BSs requires continuous optimization. However, solving the optimization problem of UAVBSs is NP-hard with no deterministic solution in polynomial time. In this paper, we propose a continuous actor-critic deep reinforcement learning solution in order to solve the location optimization problem of UAV-BSs in the presence of mobile endpoints. The simulation results show that the proposed model significantly improves the network performance compared to Qlearning, deep Q-learning and conventional algorithms. While the Q-learning and deep Q-learning-based baselines reach the sum data rate of 35 Mbps and 42 Mbps respectively, our proposed ACDQL-based strategy maximizes the sum data rate of endpoints to 45 Mbps. Furthermore, the proposed ACDQLbased methodology reduces the convergence time of the UAV-BS placement optimization by 85 percent compared to the Q-learning and deep Q-learning baselines.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".