AI-enabled Resource Allocation for BD-RIS Empowered ISAC Systems
Bibliographic record
Abstract
Reconfigurable intelligent surfaces (RIS) have emerged as pivotal elements in enhancing integrated sensing and communication (ISAC) systems. Among RIS architectures, the beyond-diagonal (BD-RIS) design stands out for its advanced beamforming capabilities compared to traditional diagonal RIS. This paper investigates the deployment of BD-RIS to optimize energy efficiency through passive and active beamforming strategies while ensuring robust communication and sensing quality. The task is particularly challenging due to constraints inherent in BD-RIS configurations, including orthogonality, quartic inequalities, and the fractional nature of the objective function. To address these complexities, we employ twin delayed deep deterministic policy gradient (TD3) models, a state-of-the-art deep reinforcement learning (DRL) approach. Numerical validations confirm the effectiveness of our proposed algorithm and highlight the benefits of integrating BD-RIS into ISAC systems. Simulations demonstrate that incorporating BD-RIS leads to significant energy efficiency gains compared to benchmarks using diagonal RIS and RIS with random phase shifts.
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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) | 0.001 | 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 | 0.001 | 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".