Outage-Constrained Secrecy Rate Maximization for STAR-RIS With Energy-Harvesting Eavesdroppers and Imperfect CSI
Bibliographic record
Abstract
This paper proposes a novel approach to enhancing secure wireless communication using a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) in a multiple-input single-output system. Unlike conventional RIS, which is limited to half-space coverage, STAR-RIS enables full 360-degree coverage, making it a promising technology for next-generation secure wireless communication. In the presence of energy-harvesting eavesdroppers, this study aims to maximize the sum secrecy rate while ensuring strict energy harvesting constraints, an area that has not been widely explored, particularly under imperfect channel state information (CSI) conditions. To tackle this challenge, we formulate a complex non-convex optimization problem, which is efficiently solved using a penalty concave-convex procedure combined with an alternating optimization algorithm. This optimization framework jointly optimizes beamforming and STAR-RIS transmission and reflection coefficients, striking a balance between secure communication and energy harvesting requirements. Numerical simulations show that the proposed method outperforms conventional RIS-based approaches, demonstrating its effectiveness even in the presence of CSI uncertainty. Furthermore, the results confirm that optimizing STAR-RIS under imperfect CSI leads to superior security and energy efficiency compared to assuming perfect CSI, highlighting the practical significance of our approach in real-world wireless networks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".