QoS-Aware Deep Unsupervised Learning for STAR-RIS Assisted Networks: A Novel Differentiable Projection Framework
Bibliographic record
Abstract
In this letter, we propose a Quality-of-Service (QoS)-aware deep unsupervised learning framework to jointly optimize beamforming and phase-shifts in a simultaneously transmitting and reflecting reconfigurable intelligence surface (STAR-RIS) assisted multi-user multi-antenna system. The objective is to maximize the downlink network sum-rate considering highly non-convex users’ minimum rate (or QoS) constraints. To satisfy constraints with zero violation, we devise a novel piece-wise differentiable projection function that projects the output of the deep neural network (DNN) to the feasible solution set of the problem. Different from the existing methods, the proposed projection function offers considerable improvement in sum-rate by enabling the search inside the feasible space. The proposed framework is general to capture both RIS and STAR-RIS-aided networks. Our proposed framework is shown to outperform genetic algorithm and existing projection-based DNNs in terms of sum-rate, time complexity, and convergence, while achieving zero probability of constraint violation.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".