QoS Control Under Perfect and Imperfect CSI in Intelligent Reflecting Surface-Assisted Multi-Cast Multi-Group Communication Systems
Bibliographic record
Abstract
To address the explosion demand for data-intensive applications, enhancing wireless transmission capacity has become crucial for today’s networks. This paper focuses on improving the quality of service (QoS) and user satisfaction in intelligent reflecting surface (IRS)-assisted multicast multi-group systems by managing the actual transmitted data instead of sending all source data. A key challenge of this problem is determining the ergodic capacity when the signal-to-noise-plus-interference (SINR) distribution in IRS-assisted wireless systems is significantly complicated. To address this issue, we propose a deep neural network (DNN)-based framework to predict the long-term network capacity accurately. We adapt well-known zero-forcing (ZF) and block diagonalization (BD) techniques to achieve efficient and secure solutions in IRS-assisted multi-cast multi-group systems. Furthermore, we consider the system in case of imperfect channel state information (CSI). Adopting the three-phase channel estimation, we propose a two-stage learning framework to enhance the accuracy of the estimated channel. Based on predicted results, we investigate adjustment algorithms to adapt to environmental changes, thus increasing the received QoS and user satisfaction. Our numerical results confirm the efficiency of the proposed design, with the channel estimation error being significantly smaller than that of the three-phase channel estimation algorithm in the literature.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".