UAV-Enabled Cooperative NOMA with Indoor- Outdoor User-Paring and SWIPT in kappa-mu Channels
Bibliographic record
Abstract
This work presents a performance analysis on cooperative non-orthogonal multiple accesses (C-NOMA) when assisted with energy harvesting enabled unmanned aerial vehicle (UAV) decode-and-forward (DF) relaying. In particular, two scenarios are considered, an outdoor-indoor one, where the NOMA signal propagates through outdoor-to-indoor, and a conventional outdoor scenario where the channel gains follow a k-u generalized fading model. The objectives of this work is to analyze the downlink performance of this C-NOMA system and derive closed-form expressions for the outage probability (OP), ergodic capacity (EC), throughput and energy efficiency (EE) for the users assuming imperfect successive interference cancellation (SIC). In particular, the OP approach considers the individual users’ rate where it is required to satisfy certain quality of service (QoS) requirements. The results provide insights into the considered performance metrics relative to key parameters such as power allocation, power splitting factor, fading parameters, and residual interference. Extensive simulations results are presented to validate the accuracy of the derived expressions.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".