Joint Power Allocation and 3D Deployment for UAV-BSs: A Game Theory Based Deep Reinforcement Learning Approach
Bibliographic record
Abstract
Ultra-dense unmanned aerial vehicle (UAV) plays an important role in the field of communications due to its flexibility and low-cost feature. Ultra-dense unnamed aerial vehicle base station (UAV-BS) can improve communication quality by providing temporary and cost-effective wireless communication services for hotspots. In this paper, a multiple UAV-BSs assisted downlink network is investigated to maximize the system throughput. It is still a challenging problem to jointly optimize the power allocation and the 3D deployment of multiple UAV-BSs. Therefore, in this paper, for effective interference management, the power allocation problem is first formulated as a non-cooperative game with a pricing mechanism to imitate the interactions among users served by UAV-BSs. Then, based on the combination of deep reinforcement learning (DRL) and the game theory, the power allocation and the 3D deployment of UAV-BSs are transformed into a Markov decision problem. Finally, a novel price-based proximal policy optimization (3PO) algorithm is proposed to explore the optimal policy to maximize the system throughput. Simulation results reveal that the proposed 3PO algorithm can significantly improve system throughput and energy efficiency compared to other baselines by jointly optimizing power allocation and 3D deployment for UAV-BSs.
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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) | 0.001 | 0.001 |
| 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 | 0.002 |
| 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".