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 to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">causal</i> channel 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 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 | 0.001 |
| Science and technology studies | 0.001 | 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".