Intelligent Reflecting Surface-based Smart Resource Management in Secure Wireless Communications
Bibliographic record
Abstract
Recently, the intelligent reflecting surface (IRS) has been considered a promising solution to address the channel blockage in wireless communication systems. However, the reflecting coefficients of IRS elements are unchanged over all communication channels, it requires a smart management mechanism to can securely support multiple users simultaneously. In this paper, we study the joint multi-IRS control and resource management to enhance the user secrecy rate. Our design aims to optimize the IRSs' coefficients, the transmit powers, and channel allocation to minimize the maximum weighted secrecy rate subject to practical constraints on the required communication rate and orthogonal transmission. To tackle the mixed-integer non-linear programming (MINLP), we propose an alternating algorithm for determining the supoptimal of the underlying problem. In particular, the IRSs' coefficients-related sub-problem and resource allocation sub-problem are iteratively solved until convergence. Furthermore, we also propose a deep neural network (DNN)-based frame-work to learn the initial point of the channel assignment in the optimization algorithm. Numerical studies confirm that the proposed design can significantly reduce the leakage ratio up to 0.1278.
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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) | 0.001 | 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".