CRB Minimization using Twin Delayed DDPG for Semi-Self Sensing Active RIS-Assisted mmWave ISAC
Bibliographic record
Abstract
This paper investigates the problem of Cramér-Rao Bound (CRB) minimization in a semi-self sensing (SS) active reconfigurable intelligent surface (RIS)-enabled integrated sensing and communication (ISAC) system in millimeter-wave (mmWave). Unlike conventional RIS, the proposed active SS-RIS architecture incorporates both reflecting and sensing elements, enabling direct radar echo reception while enhancing communication performance. A joint optimization problem is formulated to design the base station (BS) beamforming and active RIS precoding matrix with the objective of minimizing the CRB of the target's angle-of-arrival (AoA) estimation, subject to communication quality-of-service (QoS) constraints and power limitations at both the BS and RIS. Given the inherent non-convexity and computational complexity of the optimization problem, a twin delayed DDPG (TD3)-based deep reinforcement learning (DRL) framework is proposed to efficiently optimize both BS beamforming and active SS-RIS precoding matrix. Simulation results demonstrate that the proposed TD3-based approach significantly outperforms other machine learning (ML) benchmark schemes, such as deep deterministic policy gradient (DDPG) and proximal policy optimization (PPO), in terms of sensing accuracy (CRB minimization) given the present constraints. Moreover, we have shown that increasing the number of sensing elements in the SS-RIS offers substantial gains, confirming the effectiveness of active SS-RIS in enhancing both sensing and communication performance in ISAC systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".