On Performance of Non-diagonal STAR RIS with Integrated Sensing and Communications
Bibliographic record
Abstract
With the imminent arrival of 6G communication, the relevance of advanced technologies, such as multi-input multi-output (MIMO), reconfigurable intelligent surfaces (RIS), and integrated sensing and communication (ISAC), has become prominent for the plethora of Internet of Things (IoT) applications. However, integrating ISAC into MIMO networks necessitates reevaluating network performance regarding outage probability and ergodic rates. Therefore, this work provides a novel analytical framework that utilizes simultaneously transmitting and reflecting (STAR) RIS to enhance user coverage and help target detection. The framework is for downlink transmissions in the MIMO ISAC network and introduces an innovative STAR-RIS architecture, which enables the transmission/reflection of incident signals from one element to another through precise phase shift calibration. This enhances the adaptability of the RIS design, thus enhancing the system’s performance. The consequent STAR-RIS matrix exhibits non-diagonal components in contrast to the diagonal configuration found in conventional designs. The approximated outage probability expressions are derived for the users present on the reflecting side of the STAR-RIS, and the sensing rate is also evaluated to characterise the sensing performance of the targets present in the transmission side of STAR-RIS. The proposed system is demonstrated to outperform the traditional STAR-RIS system in terms of performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".