Joint Phase-Shift Design and Power Control for Near- and Far-Field Communications in Extremely Large RIS-Aided UAV Networks
Bibliographic record
Abstract
This paper investigates the integration of drone (aka UAV)-assisted networks with a reconfigurable intelligent surface (RIS) to enhance energy efficiency in near-and far-field communication scenarios. The coexistence of near-field and far-field communications introduces unique challenges in ensuring efficient resource allocation, managing interference, and meeting quality of service requirements for users. Primary users in the near-field areas have stronger signal links, while secondary users and primary far-field users face increased path loss and interference, necessitating sophisticated optimisation strategies to balance their performance. To address these challenges, we propose a joint optimisation framework for transmission power allocation and RIS phase-shift design. The framework aims to maximise energy efficiency while maintaining reliable communication for all user groups, leveraging the complementary characteristics of UAV and RIS technologies. The low-complexity optimisation approach is developed, leveraging advanced successive convex approximation techniques and iterative algorithms. The framework consists of the Dinkelbach algorithm for the outer loop and a combination of linear and convex optimisation algorithms for the inner loop. Linear programming is employed to handle the large number of variables, such as phase-reflecting coefficients, while convex programming is used to optimise power allocation in UAVs, with convergence guaranteed. Simulation results reveal significant energy efficiency gains compared to baseline methods, demonstrating the effectiveness of the proposed framework in managing the coexistence of near-and far-field communications. The findings underscore the importance of energy-efficient design in enabling scalable and sustainable UAV-assisted networks, offering valuable insights for the development of high-performance next-generation communication systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".