RL and DRL Based Distributed User Access Schemes in Multi-UAV Networks
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) have been used as aerial platform to enhance the capacity and coverage of wireless networks. The user access is challenging due to the rapid varying channels between the moving UAVs and the ground users and the interference and conflict among users. This paper aims to investigate the user access and maximize the transmission rate while guaranteeing fairness and reducing handover. The multi-user access problem is formulated to a sequential decision problem in reinforcement learning (RL). A distributed multi-armed bandit (MAB) based algorithm is proposed to address this issue. The MAB based algorithm uses straightforward reward feedback to maintain a set of probabilistic weights, which help users to make decisions. Additionally, a multi-agent proximal policy optimization (MAPPO) based algorithm in deep reinforcement learning (DRL) is employed. The MAPPO based algorithm is centrally trained and executed in a distributed manner, and it is capable of efficiently handling multi-user access decisions. Simulation results show that the MAPPO based algorithm can achieve the highest system throughput and the distributed MAB based algorithm can reduce handover and enhancing fairness. The proposed distributed algorithms outperform benchmarks in throughput and robustness significantly.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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 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".