Deployment Cost-Aware UAV and BS Collaboration in Cell-Free Integrated Aerial-Terrestrial Networks
Bibliographic record
Abstract
To enable massive connectivity and connecting the unconnected, aerial communications are becoming critical to complement with the terrestrial infrastructure. Integrated aerial-terrestrial network (IATN) offers both line-of-sight (LoS) and non-LoS (NLoS) connectivity and deployment flexibility. This paper presents a framework to optimize the deployment of aerial network and cooperation among aerial-terrestrial network such that the network deployment cost efficiency (i.e. the ratio of network sum-rate and deployment-plus-energy-cost) is maximized. 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 first formulate a Deployment Cost Efficiency (DCE) maximization problem subject to power budget, zero intra-cell pilot contamination, and UAV location constraints. We then propose a grid-based joint UAV density and location optimization, a pilot-contamination aware user clustering, and a distributed coalition game approach for clustering in C-CF-M-MIMO-enabled IATN. Complexity and convergence of the proposed algorithm are presented. Our numerical results show the efficacy of the proposed algorithm compared to conventional benchmarks. The proposed C-CF-M-MIMO-enabled IATN also outperforms terrestrial-only and aerial-only networks enabled with typical cell-free configurations, namely, (i) traditional CF-MIMO, and (ii) user-centric CF-MIMO.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".