HAPs-Assisted Cognitive Radio with UE Capability-Centered Cooperation
Bibliographic record
Abstract
In recent years, non-terrestrial network (NTN) communication has gained a significant interest with regard to its capacity to provide coverage in remote areas where deploying a terrestrial network (TN) may not be an option. A growing ecosystem between NTN service providers and TN providers is also developing in high-density urban areas where high-altitude platform stations (HAPs) can provide additional coverage. Maximizing energy efficiency of user equipment (UE) and spectral efficiency of both TN and NTN while minimizing interference to TN’s primary users (PU) and mutual interference of user equipment (UEs), all the while respecting the quality of service (QoS) constraints of all UEs is an NP-hard non-convex mixed integer problem requiring novel, adaptive, and scalable solutions. This paper presents a cooperative algorithm focused on UEs’ capabilities that optimizes sensing and transmission based on their sensing and computing capabilities to maximize their energy efficiency while respecting QoS constraints. The proposed algorithm adapts to the existing UEs or new ones joining the network by facilitating knowledge transfer among cooperative . The proposed algorithm relies on coalition structures for resource sharing, scalability, and cooperative mitigation of sensing data falsification (SDF) attacks. We test the algorithm’s performance in dense urban environments as per 3GPP specifications for channel and interference models and demonstrate the algorithm’s adaptability to new UEs entering or leaving the network.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".