Hybrid Optimization for NOMA-Based Transmissive-RIS Mounted UAV Networks
Bibliographic record
Abstract
In this work, we introduce a novel hybrid joint optimization framework specifically designed for enhancing the performance of consumer electronics in vehicular networks using a transmissive reconfigurable intelligent surface (T-RIS)-mounted uncrewed aerial vehicle (UAV) system. The UAV employs the non-orthogonal multiple access (NOMA) protocol to broadcast data to multiple ground devices, ensuring efficient communication. Our primary objective is to maximize the overall system sum rate while adhering to key constraints such as the rate requirements of ground devices, UAV battery capacity, and UAV coordinate boundaries. The optimization challenge of maximizing the system’s sum rate is inherently non-convex and complex. To address this, we decompose the problem into manageable subproblems. The beamforming optimization problem is tackled using successive convex approximation and semi-definite programming techniques, allowing for effective handling of non-convexity. For power allocation, we employ the Lagrangian dual method along with the sub-gradient technique, ensuring optimal power distribution among devices. To optimize the UAV’s location, we propose a dueling-based double deep reinforcement learning (D3RL) framework. This approach effectively combines all computed solutions, resulting in a comprehensive joint optimization strategy. Simulation results highlight the exceptional performance of the proposed framework. Specifically, optimizing the UAV’s location leads to a substantial performance gain of up to 65.9% compared to a system where only beamforming and power allocation are optimized with the UAV positioned at the center of the service area. These findings underscore the potential of our framework in advancing consumer electronics connectivity in vehicular networks.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".