Hybrid Deep Reinforcement Learning for Enhancing Localization and Communication Efficiency in RIS-Aided Cooperative ISAC Systems
Why this work is in the frame
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Bibliographic record
Abstract
In this article, we propose a novel framework that combines simultaneous localization and communication (SLAC) using a reconfigurable intelligent surface (RIS) aided integrated sensing and communication (ISAC) systems. Our primary focus is on enhancing resource efficiency in such systems. We introduce Cloud Radio Access Networks (C-RAN) that facilitate collaboration between multiple base stations (BSs), enhancing cooperation benefits for both communication and sensing capabilities. To evaluate localization performance, we formulate an optimization problem to minimize the squared position error bound (SPEB) that reflects the system functional performance by optimizing the transmit beamformer, phase shift and subcarrier assignment under certain constraints. Moreover, in order to adjust the phase shift of the RIS, we propose a RIS-aided cooperative ISAC SLAC protocol. This approach utilizes the measurements collected to refine the location and velocity estimates of the agent, as well as to reconstruct the environmental map with enhanced accuracy. However, the high dimensionality of the decision space makes the problem computationally intensive and challenging to navigate using gradient-based or exhaustive search methods. To efficiently tackle these issues, we construct a framework based on Markov decision processes (MDPs) and address it by introducing a novel algorithm called hybrid deep reinforcement learning (HDRL) algorithm. We validate our proposed algorithm through various simulations, demonstrating its effectiveness in improving system performance by comparing with the baseline schemes.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it