Phase Shift Optimization for RIS Enabled PNC System With Multiple Antennas
Bibliographic record
Abstract
Reconfigurable intelligent surfaces (RISs) have been developed to exploit the stochastic characteristics of the propagation environment. On the other hand, the integration of wireless physical network coding (PNC) and multiple antennas yields notable enhancements in system performance. This paper presents a multi-user system, employing RIS enabled PNC alongside multiple antennas to minimize both latency and error probability. Our aim is to establish reliable communication between the user pairs, which communicate through a base station (BS) via RIS. Therefore, the reflecting coefficients including both phases and amplitudes of the RISs are optimized by using the alternating direction method of multipliers (ADMM) algorithm for both single and multiple RIS cases. Extensive results are presented to compare the proposed algorithm with random phase shift, network coding (NC) and the search algorithm to illustrate the superiority of the proposed method.
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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 | 0.001 |
| 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".