Caching Placement Optimization in UAV-Assisted Cellular Networks: A Deep Reinforcement Learning-Based Framework
Why this work is in the frame
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Bibliographic record
Abstract
Capable of delivering contents offloaded from the base station (BS) to users, unmanned aerial vehicle (UAV) has emerged as a crucial leverage to compensate for terrestrial BSs-based communication. However, the limited storage capacity of the UAV brings challenges to providing low-latency services for users. In this letter, we investigate the caching placement of the UAV for enhancing the timeliness of services. To overcome the unknown content popularity, proximal policy optimization (PPO) is adopted in the proposed algorithm. To be specific, we first propose a modified timeliness model, named effective age of information (EAoI), to comprehensively evaluate the timeliness of services. Then, we employ PPO to build a deep reinforcement learning framework for finding the optimal caching strategy adaptively. Extensive simulation results are provided to verify the superiority of the proposed scheme, in comparison with the conventional schemes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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