Optimal UAV-Trajectory Design in a Dynamic Environment Using NOMA and Deep Reinforcement Learning
Bibliographic record
Abstract
Effective deployment of cellular-connected UAV networks necessitates efficient techniques to minimize mutual interference between UAVs and ground users. Moreover, the existing sub-6 GHz band suffers from extreme congestion, making it challenging to allocate unused resource blocks (RBs) for UAVs. This paper presents a learning-based UAV-path planning approach at the Base Station (BS) side, leveraging Non-Orthogonal Multiple Access (NOMA) and Deep Q-Network (DQN) methodologies to address massive connectivity and air-to-ground interference. The proposed NOMA-DQN learning approach optimizes UAV-transmission power and RB allocation jointly, taking into account the UAV-location. Additionally, it devises an interference-aware path for the UAV, considering its limited battery capacity. Simulation results demonstrate the efficacy of our proposed approach in terms of maximizing the total sum rate of aerial and ground users in a shared RB, as well as enhancing UAV energy efficiency, as compared to shortest path, orthogonal multiple-access (OMA), and random selection schemes.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".