Deep Reinforcement Learning for RIS-Aided Full-Duplex Systems: Advances and Challenges
Bibliographic record
Abstract
Deep reinforcement learning (DRL) has gained significant attention in recent years as a powerful approach for solving complex optimization problems. One of the promising applications of DRL in wireless communication is full-duplex (FD) reconfigurable intelligent surface (RIS)-assisted wireless systems, which has emerged as a potential solution for the next-generation wireless communication networks. FD-RIS-assisted systems can simultaneously transmit and receive data using the same frequency band, which can significantly improve the system capacity and spectral efficiency. This article provides an overview of the DRL background and its applications in FD-RIS-assisted communication systems. It discusses recent research advances in various scenarios, including resource allocation, sum-rate optimization, and secure communications. Furthermore, it investigates the DRL performance in optimizing large-scale FD-RIS-assisted systems. Major challenges and shortcomings of DRL in FD-RIS-assisted wireless systems are presented and supported through numerical simulations. Based on this discussion, the article highlights prospective use cases that can bring the FD-RIS-assisted systems into practice.
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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 | 0.001 | 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".