NSATC: An Interference Aware Framework for Multi-cell NOMA TUAV Airborne Provisioning
Bibliographic record
Abstract
Recently, wireless service provisioning via Unmanned Aerial Vehicles (UAVs) has emerged in 5G and beyond mobile networks. Due to the limited capacity of UAV batteries, tethered UAVs (TUAVs), which are powered from ground, are increasingly deployed in worldwide projects. However, the deployment of TUAVs in mobile networks requires high spectral efficiency, particularly in dense areas. Non-orthogonal Multiple Access (NOMA), serving users with strong channels and weak channels in the same Resource Blocks (RBs), helps overcome this issue. Due to the dynamic and massive deployment of TUAVs, inter-cell interference becomes critical. To alleviate the submerging of signals between TUAVs, we introduce a new parameter named Channel Gain Plus Interference (CGPI), reputing the interference as channel characteristics. Then, we formulate the joint optimization of power, altitude and user association. To solve this high-complexity problem, we design an algorithm, called NOMA SIC-Aware TUAV Base Station Control (NSATC) based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG). The experiment shows our proposed algorithm presents a performance enhancement between 18.8% and 121.77% of throughput and 23.76% and 51.62% of serving users than the greedy algorithm.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".