Transmit power minimization for IRS-assisted NOMA-UAV networks
Bibliographic record
Abstract
The flexibility of unmanned aerial vehicles (UAVs) allows them to be quickly deployed to support ground users. Intelligent reflecting surface (IRS) can reflect the incident signal and form passive beamforming to enhance the signal in the specific direction. Motivated by the promising benefits of both technologies, we consider a new scenario in this paper where a UAV uses non-orthogonal multiple access to serve multiple users with IRS. According to their distance to the UAV, the users are divided into the close users and remote users. The UAV hovers above the close users due to their higher rate requirement, while the IRS is deployed near the remote users to enhance their received power. We aim at minimizing the transmit power of UAV by jointly optimizing the beamforming of UAV and the phase shift of IRS while ensuring the decoding requirement. However, the problem is non-convex. Therefore, we decompose it into two sub-problems, including the transmit beamforming optimization and phase shift optimization, which are transformed into second-order cone programming and semidefinite programming, respectively. We propose an iterative algorithm to solve the two sub-problems alternatively. Simulation results prove the effectiveness of the proposed scheme in minimizing the transmit power of UAV.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".