Max-Min Fairness-Oriented Navigation and Scheduling for UAV-Enabled Wireless Networks via Deep Reinforcement Learning
Bibliographic record
Abstract
As modern wireless systems transit towards the sixth-generation (6G), uncrewed aerial vehicles (UAVs) are becoming essential in realizing ubiquitous coverage due to their fully controllable mobility and high transmission efficiency. To achieve max-min fairness among users, we aim to maximize the minimum throughput by optimizing the UAV trajectory and transmission scheduling in real-world scenarios where the UAV has access only tocausalchannel state information (CSI). However, max-min fairness is involved with the user-to-user dependency across the time horizon, which makes it challenging to maximize the minimum throughput without prior knowledge of the future CSI. To tackle this issue, we propose a bisection-integrated multi-step dueling double deep Q-network (Bi-D3QN) algorithm by combining a bisection method with a multi-step D3QN framework. The Bi-D3QN algorithm can solve the minimum throughput maximization problem by alternating between (1) finding optimal policies that minimize the UAV’s mission completion time for a given minimum throughput requirement through the multi-step D3QN and (2) evaluating the feasibility of the trained policy, followed by updating the minimum throughput requirement through the bisection method. Simulation results demonstrate that the proposed Bi-D3QN algorithm achieves a higher minimum throughput compared to existing benchmark schemes through real-time interaction with the environment.
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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 | 0.000 | 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) | 0.001 | 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".