Joint Optimization of User Association, Power Control, and Dynamic Spectrum Sharing for Integrated Aerial-Terrestrial Network
Bibliographic record
Abstract
This paper proposes a novel integrated aerial-terrestrial multi-operator network in which each operator deploys a number of unmanned aerial vehicle-base stations (UAV-BSs) besides the terrestrial macro base station (MBS), where each BS reuses the operator’s licensed band to provide downlink connectivity for UAV-user equipment (UAV-UE). In addition, the operators allow the UAV-UE, whose demand cannot be satisfied by the licensed band, to compete with others to obtain bandwidth resources from the unlicensed spectrum. Considering inter-cell and inter-operator interference in the licensed and unlicensed spectrum, the user association, power allocation, and dynamic spectrum sharing are jointly optimized to maximize the network throughput while ensuring the UAV-UEs’ data rate requirements. The formulated optimization problem, which is an NP-hard problem, is divided into two sequential subproblems. We propose a distributed iterative algorithm composed of a matching game, coalition game, and successive convex approximation technique to jointly solve the user association and power control subproblems in the licensed spectrum. Afterwards, we propose a three-layer auction framework to allocate the unlicensed spectrum dynamically between operators. Simulation results show that the proposed algorithms with the additional use of the unlicensed spectrum achieve 86.8% higher system throughput than that of only using the licensed spectrum.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".