Channel Knowledge Map-Enabled 6D Movable Antenna Systems With Kinematic Constraints: A Manifold Optimization Approach
Bibliographic record
Abstract
Six-dimensional movable antenna (6DMA) offers a potential solution to enhance wireless transmission performance by physically reconfiguring antenna positions and orientations. However, prevailing snapshot-based reactive methods are ill-suited for continuously tracking mobile user equipments (UEs) due to their neglect of antenna kinematic constraints and system latency. To address these limitations, in this paper, we propose a proactive approach by modeling UE tracking as a single, long-term 6DMA trajectory optimization problem to maximize sum spectral efficiency. Leveraging a channel knowledge map (CKM) for predictive data, our model holistically incorporates the system’s complex kinematics and physical constraints, including velocity limits and safety distances, to ensure a physically feasible trajectory. To solve this high-dimensional, non-convex problem, we develop a novel manifold optimization algorithm. This method maps the antenna’s rotational states onto the SO(3) Lie group and employs an adaptive penalty measure with tangent space backpropagation for an efficient solution. Simulation results demonstrate our approach significantly enhances sum spectral efficiency over benchmarks, while ensuring continuous and physically feasible antenna trajectories.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".