No-Regret Matching Game Algorithm for NOMA Based UAV-Assisted NB-IoT Systems
Bibliographic record
Abstract
NB-IoT is the standardized technology for machine type communication (MTC) in Long Term Evolution (LTE). NB-IoT can achieve IoT requirements nevertheless, it suffers a low rate and capacity. On the other hand, Unmanned aerial vehicles (UAV) and Non-Orthogonal Multiple Access (NOMA) are promising technology used to enhance the throughput, capacity, and coverage of wireless communication networks. In this paper, we propose a heterogeneous network scenario where a UAV small Base Station (UBS) is used to assist the LTE Macro Base Station (MBS) with the help of the Non-Orthogonal Multiple Access technique to solve the NB-IoT throughput and capacity issues. Matching game based no-regret learning algorithm is proposed to optimize the NB-IoT device association and using NOMA pairing at each base station to provide the maximum system total rate and capacity. Simulation results show that our proposed scheme increases the total rate of the system by 60% and the system capacity by at least 80%, compared to NOMA without UAV and the total rate and capacity of the system by 200% and 85% respectively, with OMA scheme.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".