HAPS-UAV-Enabled Heterogeneous Networks: A Deep Reinforcement Learning Approach
Why this work is in the frame
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Bibliographic record
Abstract
The integrated use of non-terrestrial network (NTN) entities such as the high-altitude platform station (HAPS) and low-altitude platform station (LAPS) has become essential elements in the space-air-ground integrated networks (SAGINs). However, the complexity, mobility, and heterogeneity of NTN entities and resources present various challenges from system design to deployment. This paper proposes a novel approach to designing a heterogeneous network consisting of HAPSs and unmanned aerial vehicles (UAVs) being LAPS entities. Our approach involves jointly optimizing the three-dimensional trajectory and channel allocation for aerial base stations, with a focus on ensuring fairness and the provision of quality of service (QoS) to ground users. Furthermore, we consider the load on base stations and incorporate this information into the optimization problem. The proposed approach utilizes a combination of deep reinforcement learning and fixed-point iteration techniques to determine the UAV locations and channel allocation strategies. Simulation results reveal that our proposed deep learning-based approach significantly outperforms learning-based and conventional benchmark models.
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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 | 0.001 | 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 | 0.002 | 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 it