Aerial 6D Movable Antenna-Enabled Cell-Free Networks
Bibliographic record
Abstract
In this paper, we propose an aerial movable antenna (AMA) architecture with six-dimensional (6D) spatial degrees of freedom (DoFs) to enhance the capacity of cell-free networks. Unlike conventional terrestrial access points (APs) with fixed-position antennas, the proposed AMA-enabled APs can flexibly adjust the three-dimensional (3D) unmanned aerial vehicle (UAV) positions and 3D array rotations. To overcome the high-dimensional movement-design challenges posed by multiple APs and antennas, we develop a low-overhead, low-complexity, and scalable distributed processing optimization framework to maximize the achievable uplink sum-rate of the proposed aerial cell-free network. Specifically, the team minimum mean square error (TMMSE) algorithm is proposed to design receive combiners using partial channel state information, while the weighted critic update multi-agent twin-delayed deep deterministic policy gradient (WCU-MATD3) algorithm efficiently optimizes 3D positions and 3D rotations through distributed AP collaboration. Simulation results demonstrate that the proposed AMA-enable scheme achieves a 37.1% performance gain over conventional cell-free networks with fixed antenna position and rotation by exploiting 6D spatial DoFs, while the proposed algorithm exhibits satisfactory convergence and exploration capabilities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".