Joint Trajectory and Pilot Assignment Optimization for UAV Enabled Cell-Free Massive MIMO
Bibliographic record
Abstract
Pilot assignment is a major issue in unmanned aerial vehicle (UAV) enabled user centric (UC) cell- free massive multiple input and multiple output (UC-CF-mMIMO) wireless networks where, UAVs function as distributed mobile aerial access points (MAAPs). In this paper, an efficient Tabu-search (TS) based pilot assignment scheme is proposed to address this issue where, a penalty based objective function maintains a minimum distance between mobile units (MUs). This scheme utilizes a local neighborhood search, where we first define the neighborhood structure and set the objective function. The proposed algorithm iteratively identifies a near-optimal solution with low computational overhead and achieves high average channel estimate power. We implemented a gradient based (GB) algorithm to optimize the 3D locations of MAAPs and find the signal-interference-plus-noise ratio (SINR) expression. Numerical simulations show that proposed TS-based strategy and MAAP trajectory optimization significantly enhance the per-user throughput with 95%-likelihood. The results achieved with proposed method are better than the conventional random pilot assignment schemes such as, gradient based and gibbs sampling (GB-GS), user centric perfect channel state information(UC-PSCI) and mMIMO based PCSI method while maintaining similar complexity.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".