NOMA-Enhanced Cooperative Relaying Systems in Drone-Enabled IoV: Capacity Analysis and Height Optimization
Bibliographic record
Abstract
Using the drone as a cooperative relay (CR) can improve the connectivity of Internet of Vehicles (IoV) on highways in remote areas. Meanwhile, considering limited spectrum resources, non-orthogonal multiple access (NOMA) is regarded as a promising approach for increasing spectrum efficiency and the channel capacity of IoV. Motivated by the above, this paper investigates the application of the cooperative relaying system (CRS) and NOMA in drone-enabled IoV. Specifically, we propose a NOMA-enhanced CRS in drone-enabled IoV for spatially multiplexed transmissions, where the base station is allowed to transmit two different data symbols simultaneously to associated vehicles. Next, in order to analyze the capacity, we adopt the incomplete Gamma function and Taylor expansion of Bessel function to derive the closed-form expression of average data rates. Then, to simplify the calculation, we provide an approximate expression by using the Gauss-Chebyshev integral. Afterwards, to fully exploit the advantages of the CR, the CR height is optimized by employing the Steffensen method, which has second-order convergence without calculating the derivative. In order to apply to real scenarios, we extend the considered model to accommodate large-scale networks by using the stochastic geometric method. Finally, the simulation results show the accuracy of the obtained approximate results, where the performance gap is 1.6%. Moreover, the NOMA-enhanced CRS using the proposed CR height optimization scheme outperforms the current works in terms of the sum data rate. Furthermore, utilizing the Steffensen method can reduce the running time by 26.3% in comparison with the Newton method.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".