PPO-Based Energy Efficiency Maximization For RIS-Assisted Multi-User Miso Systems
Bibliographic record
Abstract
In this paper, we explore the integration of a reconfigurable intelligent surface (RIS) with a multi-antenna base station (BS) for downlink multi-user multiple-input-single-output (MU-MISO) systems. We aim to enhance energy efficiency (EE) by jointly optimizing beamforming and phase shifts at the BS and RIS, respectively, while ensuring each mobile user meets their link budget requirements. The resulting optimization problem is inherently non-convex. To address this challenge, we employ proximal policy optimization (PPO), known for efficiently managing non-convex problems and reducing training overhead in continuous action spaces through a clip factor. Furthermore, by leveraging deep neural networks (DNN), the proposed PPO-based solution provides the optimum values for the beamforming at the BS and the phase shift at the RIS, respectively. Finally, we demonstrate the effectiveness and accuracy of the proposed PPO-based algorithm through an extensive simulation campaign, comparing its performance against baseline methods (i.e., fractional programming (FP) and deep deterministic policy gradient (DDPG)). The results show that our proposed PPO-based algorithm outperforms the considered baseline approaches (i.e., FP and DDPG) in terms of EE by 34.2% and 15.8%, respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".