Transmission Design and Optimization for STAR-RIS-Assisted Symbiotic Radio Systems
Bibliographic record
Abstract
This paper develops a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted symbiotic radio (SR) system, in which the STAR-RIS is deployed to transmit extra Internet of Things (IoT) data and simultaneously enhance the downlink transmission. A simple and efficient ON-OFF keying modulation scheme is applied by the STAR-RIS to modulate IoT data, which allows a low-complexity IoT transceiver and avoids the signal ambiguity. This work aims to maximize the weighted sum-rate (WSR) of downlink users, subject to the minimum received energy requirement of IoT transmission. Under the assumption of perfect channel state information (CSI), an efficient penalty dual decomposition (PDD)-based algorithm is proposed to solve the WSR maximization problem. By leveraging the PDD framework, the STAR-RIS’s coefficients are updated with close-form expressions. For the imperfect CSI case, the WSR maximization problem becomes a challenging stochastic optimization task. To address it, the constrained stochastic successive convex approximation framework is employed. Additionally, an efficient projection method is proposed to handle the STAR-RIS’s amplitude and coupled phase-shift constraints. Simulation results reveal the performance trade-off between the downlink transmission and the IoT transmission and validate the superiority of the proposed algorithms over the benchmarks.
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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 | 0.001 | 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".