On Discrete Phase Shifts Optimization of RIS-Aided FD Systems: Are All RIS Elements Needed?
Bibliographic record
Abstract
This paper investigates a practical distributed reconfigurable intelligent surface (RIS)-assisted full-duplex wireless system. For the first time in the literature, the system resources minimization problem is considered by jointly optimizing the RIS phase shifts and their states (ON/OFF) subject to target sum rate constraints. The paper further considers a discrete phase shift model at the RISs. As the formulated problem is mixed-integer and non-convex, it is decoupled into two sub-problems: transmit beamforming and joint RIS phase shifts and RIS elements state optimization. The former problem is mathematically addressed using approximate solutions, while the latter problem is addressed using a novel reinforcement learning (RL) approach. Simulation results illustrate that the proposed RL algorithm is flexible for different target rate constraints. The results further show that the proposed framework efficiently saves a considerable number of reflecting elements by configuring their state.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".