RSS-Based UAV-BS 3-D Mobility Management via Policy Gradient Deep\n Reinforcement Learning
Bibliographic record
Abstract
We address the mobility management of an autonomous UAV-mounted base station\n(UAV-BS) that provides communication services to a cluster of users on the\nground while the geographical characteristics (e.g., location and boundary) of\nthe cluster, the geographical locations of the users, and the characteristics\nof the radio environment are unknown. UAVBS solely exploits the received signal\nstrengths (RSS) from the users and accordingly chooses its (continuous) 3-D\nspeed to constructively navigate, i.e., improving the transmitted data rate. To\ncompensate for the lack of a model, we adopt policy gradient deep reinforcement\nlearning. As our approach does not rely on any particular information about the\nusers as well as the radio environment, it is flexible and respects the privacy\nconcerns. Our experiments indicate that despite the minimum available\ninformation the UAV-BS is able to distinguish between high-rise (often\nnon-line-of-sight dominant) and sub-urban (mainly line-of-sight dominant)\nenvironments such that in the former (resp. latter) it tends to reduce (resp.\nincrease) its height and stays close (resp. far) to the cluster. We further\nobserve that the choice of the reward function affects the speed and the\nability of the agent to adhere to the problem constraints without affecting the\ndelivered data rate.\n
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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 | 0.001 | 0.002 |
| 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".