Coalition Formation Game for UAV-BS Cooperation in Cell-Free Integrated Aerial-Terrestrial Networks
Bibliographic record
Abstract
In order to facilitate massive connectivity and connecting the unconnected, aerial communications are becoming increasingly essential as a complement to terrestrial infrastructure. The integrated aerial-terrestrial network (IATN) offers both line-of-sight (LoS) and non-LoS (NLoS) connectivity and flexible deployment. This paper introduces a framework designed to optimize the cooperation between aerial and terrestrial networks, with the goal of maximizing the deployment cost efficiency (DCE) of the network (i.e., the ratio of the network's total data transmission rate to the combined deployment and energy costs). The cooperation among unmanned aerial vehicles (UAVs) and terrestrial base-station (BSs) is supported with clustered cell-free massive MIMO (C-CF-M-MIMO). Specifically, we formulate a problem focused on maximizing the DCE while adhering to power constraints and zero intra-cell pilot contamination. Subsequently, we propose a pilot-contamination aware user clustering, and a distributed coalition formation game for BSs and UAVs clustering in C-CF-M-MIMO-enabled IATN. Our numerical findings demonstrate the efficacy of the proposed algorithm when compared to conventional benchmark methods. Furthermore, the C-CF-M-MIMO-enabled IATN outperforms BSs-only and UAVs-only network equipped with typical cell-free configurations, such as (i) traditional CF-MIMO and (ii) user-centric CF-MIMO.
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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".