Game theoretic models for the analysis of UAV-aided wireless communications
Bibliographic record
Abstract
Aerial networks, utilizing Unmanned Aerial Vehicles (UAVs) have recently gained significant attention as they will play a key role in shaping the future of wireless networks including beyond 5G and 6G. This thesis addresses challenges in utilizing UAVs for wireless communication networks, focusing on two deployment scenarios: UAV-only networks and integrated aerial-terrestrial networks (IATN) with both UAVs and base stations (BSs). The first deployment scenario is applicable in situations like natural disasters, where UAV deployment is well-suited for establishing temporary infrastructure. In such cases, optimizing resource utilization is important. Further, in such scenarios, there could be a limited availability of information. Therefore, we study the resource sharing problem in a UAVs-based network under uncertainty. Specifically, the UAVs cooperate in serving the users while pooling their spectrum and energy resources in the absence of prior knowledge about different system characteristics such as the amount of available power at the other UAVs. Regarding solutions, centralized management requires comprehensive global network information accessible to a central controller for optimization. These methods, however, suffer from excessive overhead and computational cost. Therefore, we utilize Bayesian Coalition Formation Game (BCFG) to address the resource sharing problem in a cooperative UAV network with uncertainty. The second scenario involves leveraging UAVs to complement terrestrial networks, enhancing connectivity through unique features like enhanced line-of-sight, mobility, and flexibility. Consequently, efficient cooperation between aerial and terrestrial networks holds the potential to introduce an additional dimension for enhancing the user experience and optimizing network resource utilization as users can utilize both LoS and non-LoS channels, different altitudes, and types of BSs. Therefore, we present 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 UAVs and BSs is supported with clustered cell-free-massive-MIMO (C-CF-M-MIMO). Our approach involves a grid-based joint UAV density and location optimization, pilot-contamination aware user clustering, and distributed coalition game for UAVs and BSs cooperation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".