Deep Reinforcement Learning‐Based Multireconfigurable Intelligent Surface for MEC Offloading
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Bibliographic record
Abstract
Computational offloading in mobile edge computing (MEC) systems provides an efficient solution for resource‐intensive applications on devices. However, the frequent communication between devices and edge servers increases the traffic within the network, thereby hindering significant improvements in latency. Furthermore, the benefits of MEC cannot be fully realized when the communication link utilized for offloading tasks experiences severe attenuation. Fortunately, reconfigurable intelligent surfaces (RISs) can mitigate propagation‐induced impairments by adjusting the phase shifts imposed on the incident signals using their passive reflecting elements. This paper investigates the performance gains achieved by deploying multiple RISs in MEC systems under energy‐constrained conditions to minimize the overall system latency. Considering the high coupling among variables such as the selection of multiple RISs, optimization of their phase shifts, transmit power, and MEC offloading volume, the problem is formulated as a nonconvex problem. We propose two approaches to address this problem. First, we employ an alternating optimization approach based on semidefinite relaxation (AO‐SDR) to decompose the original problem into two subproblems, enabling the alternating optimization of multi‐RIS communication and MEC offloading volume. Second, due to its capability to model and learn the optimal phase adjustment strategies adaptively in dynamic and uncertain environments, deep reinforcement learning (DRL) offers a promising approach to enhance the performance of phase optimization strategies. We leverage DRL to address the joint design of MEC‐offloading volume and multi‐RIS communication. Extensive simulations and numerical analysis results demonstrate that compared to conventional MEC systems without RIS assistance, the multi‐RIS‐assisted schemes based on the AO‐SDR and DRL methods achieve a reduction in latency by 23.5% and 29.6%, respectively.
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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 | 0.000 |
| Open science | 0.001 | 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