Proximal Policy Optimization based sum rate maximization scheme for STAR-RIS-assisted vehicular networks underlaying UAV
Bibliographic record
Abstract
The consumer electronics industry is undergoing significant transformations due to the ongoing advancements in mobile Internet technology, 5G, Internet of Things (IoT), artificial intelligence (AI), and other emerging technologies. Additionally, the development of intelligent electronic products is accelerating. Higher communication quality is required as a result of the convergence of consumer electronics and developing technologies. The low cost and simple deployment of the Simultaneous Transmitting and Reflecting Reconfigurable intelligent surface (STAR-RIS) can show considerable possibilities. STAR-RIS is a well-known for potentially improving wireless network performance. STAR-RIS enables users positioned on different sides of the surfaces to simultaneously receive signals that are transmitted or reflected. In this article, we examines the difficulties of sum rate maximization in a STAR-RIS assisted downlink network with NOMA assistance, where the incident signal energy at STAR-RIS is divided into two halves for transmitting and reflecting. This dynamic nature of wireless networks makes it challenging to tackle the sum rate maximization problem using the conventional approach of convex optimization techniques. To overcome the difficulties of the sum rate, the proposed scheme uses the Proximal Policy Optimization (PPO) based algorithm based on Deep Reinforcement Learning (DRL) which optimizes the beamforming vectors at the base station and the coefficient matrices and symbol rate at the STAR-RIS. Finally, the performance evaluation demonstrates that the proposed scheme maximizes the system sum rate while considering time-varying channels into account, and the PPO-based algorithm performs better than the Deep Deterministic Policy Gradient (DDPG) algorithm. Also, the results shows that the proposed scheme has 22.05%, 35.12% and 48.9% higher sum rate as compared to DDPG, Zero forcing and random.
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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 | 0.002 |
| 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 | 0.002 |
| 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".