Intelligent Reflecting Surface-based Secrecy Rate Enhancement in Multicast Multigroup Tactical Communication Systems
Bibliographic record
Abstract
Improving wireless transmission capacity and security is substantial in today's tactical networks due to the explosion of the data-intensive applications. In this paper, we investigate a framework that can successfully convey the messages from the transmitter to the receivers with the supports of intelligent reflecting surfaces (IRSs) to achieve high data rate. In particular, we manage the actual transmitted data instead of sending all source data in IRSs-aided multicast multigroup systems. A key challenging issue of this problem consists in obtaining a closed-form expression for the complicated distribution of the signal-to-noise-plus-interference (SINR) in IRSs-aided wireless systems. Therefore, we propose a deep neural network (DNN)-based framework to obtain a high-accuracy prediction of the long-term network capacity. Based on predicted results, we solve the joint power control and data reduction ratio selection problem to maximize the total received quality of service (QoS). Furthermore, we also consider the physical layer security design where data leakage among users' groups is prevented. We adapt well-known zero-forcing (BD) and block diagonalization (BD) techniques in achieving high-efficient secure solutions in IRSs-aided multicast multi-group systems. Numerical results confirm the efficiency of the proposed design. The information leakage can decrease down to 0 thanks to our proposed framework.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 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".