Energy-Efficient Joint Broadcast-Unicast Communications via Aerial RIS
Bibliographic record
Abstract
This paper proposes a joint broadcast-unicast downlink communication framework implementing layered-division multiplexing (LDM) and aerial reconfigurable intelligent surface (RIS), and develops a corresponding design with a focus on energy efficiency. Maximization of the energy efficiency is achieved by jointly designing the optimal active beamforming at the base station and the passive beamforming at the aerial RIS. The non-convex optimization is solved using a two-stage algorithm. The active beamforming is designed by using low-complexity zero-forcing for the unicasting service and successive convex approximation based on the first-order Taylor series expansion for the broadcasting service, and the RIS phase shifting is designed with semi-definite programming. Comparative results are provided, and show that the proposed LDM-based RIS-assisted joint broadcast-unicast communication framework is more energy efficient than the joint broadcast-unicast via time-division multiplexing or amplify-and-forward relaying.
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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 | 0.000 | 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 | 0.001 |
| 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".