Energy-Efficient Resource Allocation in Multi-UAV Networks With NOMA
Bibliographic record
Abstract
This paper investigates the energy efficiency (EE) optimization in a wireless communication network where multiple UAVs serve different types of devices, namely, information receivers (IRs) and energy receivers (ERs). The UAVs transmit power signals towards the ERs, and then enable data transmission to IRs on the downlink and from ERs on the uplink with non-orthogonal multiple access (NOMA). The optimization problem to maximize the overall EE is formulated and solved using Lagrangian optimization and gradient-descent methods. The optimization is decomposed into two sub-problems. Firstly, by connecting the path loss of the devices’ channels with their rate demands, the UAVs’ optimal positions are obtained. Then, based on the obtained UAVs’ optimal positions and a closed-form expression for the EE, a resource allocation aiming to maximize EE is developed. For the simulations, two main scenarios for single and multiple UAVs are considered. Numerical results and comparisons are provided. In particular, for the single-UAV scenario, the results show an enhancement in EE for the operation with NOMA compared with OMA. For the multiple-UAV scenario, several cases depending on different combinations of the devices’ rate requirements are considered. The results show the superiority of NOMA over OMA in all use cases. The results also reveal the effect of considering the devices’ rate requirements on the EE, where the case with equal rate requirements has the best performance.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".