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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".