Active and STAR-RIS-Assisted MIMO ISAC Systems With SWIPT
Bibliographic record
Abstract
As an innovative framework for sustainable communication within next-generation networks, reconfigurable intelligent surfaces (RISs) have developed potential transformation in improving simultaneous wireless information and power transfer (SWIPT). Alongside SWIPT, integrated sensing and communication (ISAC) has gained significant attention for its ability to combine communication and sensing functionalities within the same infrastructure, optimizing resource utilization and improving system performance. In this paper, we propose a framework that integrates an active (A-RIS) and simultaneous transmitting and reflecting RIS (STAR-RIS) assisted multiple-input multiple-output (MIMO) technology for SWIPT in an ISAC system. The system consists of an A-RIS, a STAR-RIS, a dual functional base station (DFBS), a group of reflection and transmission communication users (CUs) and reflection and transmission energy receiving devices (EDs), sensing targets simultaneously with the aid of RIS. We formulate an optimization problem to maximize the target rate while balancing the communication rate under energy harvesting (EH) constraints, and phase-shifts constraints. It implies the trade-offs between target sensing accuracy and communication performance, demonstrating the potential of RIS to enhance system capabilities in diverse ISAC scenarios. The optimization problem and its constraints are inherently non-convex due to the high coupling between variables. Consequently, we employ the minimum mean square error method to address the non-convex nature of the problem. Thereby, it simplifies the problem by transforming it into a more manageable form and then applying an alternating optimization framework. This framework addresses the design of beamforming challenges at both the DFBS and the RIS (A-RIS/STAR-RIS) separately, by solving them iteratively using general approximation techniques. The analysis highlights the advantages of the proposed ARIS and STAR-RIS assisted SWIPT for ISAC framework over conventional RIS by achieving performance gain of 12-15%.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".