Beamforming for Maximal Coverage in mmWave Drones: A Reinforcement Learning Approach
Bibliographic record
Abstract
Drone as a base station can provide wireless services in a variety of situations. In this letter, we employ a uniform linear array (ULA) to produce a directional beam to increase the quality of service (QoS) of users in the downlink of cellular networks. Due to the strict power limitations of a drone base station (DBS), we envision a single radio frequency (RF) chain architecture. A beamforming design methodology in an unknown environment is presented over a mmWave channel with the aim of maximizing the number of covered users while taking into account the human body blockage effects. Regarding the ambiguity of the environment, we model the problem of finding the optimal beam direction as a multi-armed bandit (MAB). Due to its fast convergence property, Thompson sampling (TS) is used for solving the MAB problem. Simulation results show that the DBS is able to find the optimal beam angle in only tens of iterations.
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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 | 0.000 |
| 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".