Resource Allocation for Integrated Sensing and Communication in Digital Twin Enabled Internet of Vehicles
Why this work is in the frame
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Bibliographic record
Abstract
With the development of the sixth-generation (6G) network, virtualization remains critical. The key to future virtualization lies in the service provisioning capability of the network and the service requirements of end users, which will lead to virtualization of the network and end users. Therefore, this paper proposes a holistic network virtualization architecture that integrates digital twin (DT) and network slicing to achieve the network management of service-centric and user-centric. With the explosive growth of latency-sensitive and computing-intensive in-vehicle applications, limited in-vehicle computing resources are difficult to meet diverse network requirements, and vehicle edge computing (VEC) has become a potential solution. However, computation offloading may face the dilemma of excessive upload traffic and unbearable upload time. Therefore, in order to minimize the overall response time (ORT) of the system, this paper proposes a new environment aware offloading mechanism (EAOM) based on the integrated sensing and communication system (ISAC) to solve the joint optimization problem of task scheduling and resource allocation. Considering the mobility of vehicles and the time-varying of environment, the optimization problem is modeled as a Markov decision process, and an improved algorithm combining Shapley-Q value and deep deterministic policy gradient (DDPG) is used to solve it. The simulation results indicate the effectiveness and superiority of the scheme proposed in our work.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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