Mode Switching-Based STAR-RIS With Discrete Phase Shifters
Why this work is in the frame
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Bibliographic record
Abstract
The increasing demand for cost-effective, high-speed Internet of Things (IoT) applications in the coming sixth-generation (6G) networks has driven research toward maximizing spectral efficiency and simplifying hardware designs. In this context, we investigate the sum rate maximization problem for a mode-switching discrete-phase shifters simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided multi-antenna access point network, emphasizing hardware efficiency and reduced cost. A mixed-integer nonlinear optimization framework is formulated for joint optimization of the active beamforming matrix, user power allocation, and STAR-RIS phase shift vectors, including binary transmission/reflection amplitudes and discrete phase shifters. To solve the formulated problem, we employ a block coordinate descent method, dividing it into three subproblems tackled using difference-of-concave programming and combinatorial optimization techniques. Numerical results validate the effectiveness of the proposed joint optimization approach, consistently achieving superior sum rate performance compared to partial optimization methods, thereby underscoring its potential for efficient and scalable 6G IoT systems.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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 it