Interference Management in Cellular-Connected Internet of Drones Networks With Drone-Pairing and Uplink Rate-Splitting Multiple Access
Why this work is in the frame
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Bibliographic record
Abstract
Interference management is a key challenge for cellular-connected Internet of Drones (IoD) networks that employ multiple cellular-connected hovering drones for data acquisition in surveillance and monitoring applications. This article proposes a novel resource optimization framework for managing interference in cellular-connected IoD networks. Specifically, the envisioned system divides the set of transmitting drones into distinct drone pairs, where the paired drones simultaneously transmit over the same radio resource blocks (RRBs). Each drone pair is assigned a set of orthogonal RRBs for data transmission, where these RRBs are shared with the terrestrial cellular network as well. An uplink rate-splitting multiple access scheme is employed to mitigate the interdrone interference at the drone pairs, and an RRB pricing method is exploited to control the interference between the aerial and cellular communication links. Our goal is to maximize the uplink capacity of the IoD network while reducing interference over the shared RRBs between the IoD and cellular networks. Toward this goal, a joint optimization of the drones’ transmit power allocation, drone pairing, and RRB scheduling among the drone pairs is presented. In order to obtain an efficient suboptimal solution, an iterative optimization is devised. Particularly, the presented joint optimization problem is decomposed into three subproblems for transmit power allocation, drone pairing and RRB scheduling, and RRB price update. By solving theses subproblems iteratively, a convergent rate-splitting-empowered resource allocation and clustering for interference management (REACT) algorithm is proposed. Extensive simulations are conducted to verify the effectiveness of the proposed REACT algorithm over several benchmark schemes.
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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 it